Sample records for parameters predict etiologic

Full text: Linearity of the microwave parameters (resonance linewidth ΔH and effective linewidth ΔH eff ) is demonstrated and their use in the Computer-aided design (CAD)/Computer-aided manufacturing (CAM) of new microwave garnets is proposed. Such an approach would combine a numerical database of microwave data and several computational programs. The model is an applied formulation of the analysis of a wide range of microwave garnets

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Full Text Available Materials and methods. 18 native humic acids (HAs were received from nine representative types of peat of the Tomsk region. Two extraction methods were used: sodium hydroxide and sodium pyrophosphate. Molecular structure parameters were investigated by IR-spectroscopy. The assessement of qualitative and quantitative features of the IR-spectra of 18 different humic acids was made. When HAs with mouse macrophages were cultured their ability to influence the NO-stimulation was determined. Thus, the biological activity of HAs and its dependence on the parameters of the molecular structure were studied.Results. The results of infrared spectroscopy showed that the HAs of upland types of peat contain more carbonyl, carboxyl, and ester groups, and HAs of lowland types of peat contain more aromatic carbon, phenolic and alcoholic hydroxyl, ether and carbohydrate fragments. The results of biological activity showed that HAs from upland types of peat induce the formation of nitrogen oxide, wherein the cell activation decreases with HAs obtained by alkali. All types of HAs from lowland types of peat contain an admixture of endotoxin. Some HAs obtained by sodium pyrophosphate have higher immunotropic activity; the HAs can cause antigen-specific stimulation of cells. The activity of HAs does not depend on endotoxin admixture. The results of molecular spectroscopy showed that the most biologically active HAs have higher aromaticity and higher concentration of oxygen-containing functional groups. This result can be used as a marker factor in the standardization of HAs.

No etiologicalprediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; the area under the curve was used to evaluate discriminatory ability of models. Area under the curve was 0.93 for the full model (including age, smoking and coffee habits, DNA adducts, 12 genotypes) and 0.86 for the short model (including smoking, DNA adducts, 3 genotypes). Using the "best cut-off" of predicted probability of a positive outcome, percentage of cases correctly classified was 92% (full model) against 75% (short model). Cancers classified as "positive outcome" are those to be referred for evaluation by an occupational physician for etiological diagnosis; these patients were 28 (full model) or 60 (short model). Using 3 genotypes instead of 12 can double the number of patients with suspect of aromatic amine related cancer, thus increasing costs of etiologic appraisal. Integrating clinical, laboratory and genetic factors, we developed the first etiologicprediction model for aromatic amine related bladder cancer. Discriminatory ability was excellent, particularly for the full model, allowing individualized predictions. Validation of our model in external populations is essential for practical use in the clinical setting.

Background: The objective of this study was to identify the prognostic factors that influence the outcome of ovarian stimulation with intrauterine insemination (IUI) cycles in couples with different infertility etiology. Materials and Methods: This retrospective study was performed in data of 1348 IUI cycles with ovarian stimulation by clomiphene citrate (CC) and/or gonadotropins in 632 women with five different infertility etiology subgroups at Akbarabbadi Hospital, Tehran, Iran. Results: The pregnancy rate (PR)/ cycle was highest (19.9%) among couples with unexplained infertility and lowest (10.6%) in couples with multiple factors infertility. In cases of unexplained infertility, the best PRs were seen after CC plus gonadotropins stimulation (26.3%) and with inseminated motile sperm count>30×106 (21.9%), but the tendency didn’t reach statistical significant. In the ovarian factor group, the best PRs were observed in women aged between 30 and 34 years (20.8%), with 2-3 preovulatory follicles (37.8%) and infertility duration between 1and 3 years (20.8%), while only infertility duration (p=0.03) and number of preovulatory follicles (p=0.01) were statistically significant. Multiple logistic regression analysis determined that number of preovulatory follicles (p=0.02), duration of infertility (p=0.015), age (p=0.019), infertility etiology (p=0.05) and stimulation regimen (p=0.01) were significant independent factors in order to predict overall clinical PR. Conclusion: The etiology of infertility is important to achieve remarkable IUI success. It is worth mentioning that within different etiologies of infertility, the demographic and cycles characteristics of couples did not show the same effect. Favorable variables for treatment success are as follows: age infertility ≤5 years and a cause of infertility except of multiple factors. PMID:24520471

Full Text Available Accumulating evidence has recently supported the association of bacterial infection with the growth and development of cancers, particularly in organs that are constantly exposed to bacteria such as the lungs, colon, cervical cancer etc. Our in silico study on the proteome of Chlamydia pneumoniae suggests an unprecedented idea of the etiology of lung cancer and have revealed that the infection of C. pneumoniae is associated with lung cancer development and growth. It is reasonable to assume that C. pneumoniae transports its proteins within host-intracellular organelles during infection, where they may work with host-cell proteome. The current study was performed for the prediction of nuclear targeting protein of C. pneumoniae in the host cell using bioinformatics predictors including ExPASy pI/Mw tool, nuclear localization signal (NLS mapper, balanced sub cellular localization predictor (BaCeILo, and Hum-mPLoc 2.0. We predicted 47/1112 nuclear-targeting proteins of C. pneumoniae connected with several possible alterations in host replication and transcription during intracellular infection. These nuclear-targeting proteins may direct to competitive interactions of host and C. pneumoniae proteins with the availability of same substrate and may be involved as etiological agents in the growth and development of lung cancer. These novel findings are expected to access in better understanding of lung cancer etiology and identifying molecular targets for therapy.

Full Text Available We reevaluate the Burton equation (Burton et al. 1975 of predicting Dst index using high quality hourly solar wind data supplied by the ACE satellite for the period from 1998 to 2006. Sixty magnetic storms with monotonously decreasing main phase are selected. In order to determine the injection term (Q and the decay time (tau of the equation, we examine the relationships between Dst* and VB_s, Delta Dst* and VB_s, and Delta Dst* and Dst* during the magnetic storms. For this analysis, we take into account one hour of the propagation time from the ACE satellite to the magnetopause, and a half hour of the response time of the magnetosphere/ring current to the solar wind forcing. The injection term is found to be Q({nT}/h=-3.56VB_s for VB_s>0.5mV/m and Q({nT}/h=0 for VB_s leq0.5mV/m. The tau (hour is estimated as 0.060 Dst* + 16.65 for Dst*>-175nT and 6.15 hours for Dst* leq -175nT. Based on these empirical relationships, we predict the 60 magnetic storms and find that the correlation coefficient between the observed and predicted Dst* is 0.88. To evaluate the performance of our prediction scheme, the 60 magnetic storms are predicted again using the models by Burton et al. (1975 and O'Brien & McPherron (2000a. The correlation coefficients thus obtained are 0.85, the same value for both of the two models. In this respect, our model is slightly improved over the other two models as far as the correlation coefficients is concerned. Particularly our model does a better job than the other two models in predicting intense magnetic storms (Dst* lesssim -200nT.

-averaged acoustical data. The results are presented in the form of linear, multiple regression formulas that may be used to predict the values of the newer measures of level, clarity, spaciousness, and musicians' conditions on the orchestra platform in halls with given RT and geometry....

Runway-navigation-monitor (RNM) and critical-distances-process electronic equipment designed to provide pilot with timely and reliable predictive navigation information relating to takeoff, landing and runway-turnoff operations. Enables pilot to make critical decisions about runway maneuvers with high confidence during emergencies. Utilizes ground-referenced position data only to drive purely navigational monitor system independent of statuses of systems in aircraft.

Scrub typhus usually presents as acute undifferentiated fever. This cross-sectional study included adult patients presenting with acute undifferentiated fever defined as any febrile illness for ≤ 14 days without evidence of localized infection. Scrub typhus cases were defined by an antibody titer of a ≥ fourfold increase in paired sera, a ≥ 1:160 in a single serum using indirect immunofluorescence assay, or a positive result of the immunochromatographic test. Multiple regression analysis identified predictors associated with scrub typhus to develop a prediction rule. Of 250 cases with known etiology of acute undifferentiated fever, influenza (28.0%), hepatitis A (25.2%), and scrub typhus (16.4%) were major causes. A prediction rule for identifying suspected cases of scrub typhus consisted of age ≥ 65 years (two points), recent fieldwork/outdoor activities (one point), onset of illness during an outbreak period (two points), myalgia (one point), and eschar (two points). The c statistic was 0.977 (95% confidence interval = 0.960–0.994). At a cutoff value ≥ 4, the sensitivity and specificity were 92.7% (79.0–98.1%) and 90.9% (86.0–94.3%), respectively. Scrub typhus, the third leading cause of acute undifferentiated fever in our region, can be identified early using the prediction rule. PMID:25448236

Current study determined the overall incidence, common causes as well as main predictors of this final diagnosis among neonates admitted to a rural district hospital in Iran. This study was conducted on 699 neonates who were candidate for admission to the NICU. Study population was categorized in the case group, including patients exposed to final diagnosis of neonatal seizures and the control group without this diagnosis. Neonatal seizure was reported as final diagnosis in 25 (3.6%) of neonates. The most frequent discharge diagnosis in the seizure group was neonatal sepsis and in the non-seizure group was respiratory problems. No significant difference was found in early fatality rate between neonates with and without seizures (8.0% vs. 10.1%). Only gestational age <38 week had a relationship with the appearance of neonatal seizure. Low gestational age has a crucial role for predicting appearance of seizure in Iranian neonates.

parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while

analysis was used to determine the predictive factors associated with abnormal semen parameters. .... for frequency, mean and χ2 with the level of significance set at p<0.05. ... was obtained from each couple participating in the study, following.

The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome

Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.

Conclusion: This study demonstrates that the point-of-care test Alere Test Pack +Plus Strep A has a high positive predictive value and is able to rule in GAS infection as long as the proportion of carriers is low. Also the negative predictive value for ruling out GAS as the etiologic agent is very high irrespective of the carrier rate. Hence, this test is always useful to rule out GAS infection.

The prediction performances of several climatological models, including the ionospheric conductivity and electron density model, RADAR C, and Ionospheric Communications Analysis and Predictions Program, are evaluated for different regions and sunspot number inputs. Particular attention is given to the near-real-time (NRT) predictions associated with single-station updates. It is shown that a dramatic improvement can be obtained by using single-station ionospheric data to update the driving parameters for an ionospheric model for NRT predictions of f(0)F2 and other ionospheric and HF circuit parameters. For middle latitudes, the improvement extends out thousands of kilometers from the update point to points of comparable corrected geomagnetic latitude. 10 refs

During the past 15 years, flow assisted corrosion has been a worldwide problem in the power generating industry. The phenomena is complex and depends on environment, material composition, and hydrodynamic factors. Recently, modeling of flow assisted corrosion has become a subject of great importance. A key part of this effort is modeling the hydrodynamic aspects of this issue. This paper examines which hydrodynamic parameter should be used to correlate the occurrence and rate of flow assisted corrosion with physically meaningful parameters, discusses ways of measuring the relevant hydrodynamic parameter, and describes how the hydrodynamic data is incorporated into the predictive model

Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

Software based on molecular structural mechanics approach (MSMA) and using finite element method (FEM) has been developed to predict the Young's modulus of graphene sheets. Obtained results have been compared to results available in the literature and good agreement has been shown when the same values of uncertainty parameters are used. A sensibility of the models to their uncertainty parameters has been investigated using a stochastic finite element method (SFEM). The different values of the used uncertainty parameters, such as molecular mechanics force field constants k_r and k_θ, thickness (t) of a graphene sheet and length ( L_B) of a carbon carbon bonds, have been collected from the literature. Strong sensibilities of 91% to the thickness and of 21% to the stretching force (k_r) have been shown. The results justify the great difference between Young's modulus predicted values of the graphene sheets and their large disagreement with experimental results.

The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ (j) of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ (j) , we assume that φ (j) depends on φ (j−m) , φ (j−m+1) ,…, φ (j−1) and the interest rate r j+n at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r j+n+1 of the interest rate at the next time point when the value r j+n of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r j+n+d at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters

The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.

Low mean fundamental frequency (F(0)) in men's voices has been found to positively influence perceptions of dominance by men and attractiveness by women using standardized speech. Using natural speech obtained during an ecologically valid social interaction, we examined relationships between multiple vocal parameters and dominance and attractiveness judgments. Male voices from an unscripted dating game were judged by men for physical and social dominance and by women in fertile and non-fertile menstrual cycle phases for desirability in short-term and long-term relationships. Five vocal parameters were analyzed: mean F(0) (an acoustic correlate of vocal fold size), F(0) variation, intensity (loudness), utterance duration, and formant dispersion (D(f), an acoustic correlate of vocal tract length). Parallel but separate ratings of speech transcripts served as controls for content. Multiple regression analyses were used to examine the independent contributions of each of the predictors. Physical dominance was predicted by low F(0) variation and physically dominant word content. Social dominance was predicted only by socially dominant word content. Ratings of attractiveness by women were predicted by low mean F(0), low D(f), high intensity, and attractive word content across cycle phase and mating context. Low D(f) was perceived as attractive by fertile-phase women only. We hypothesize that competitors and potential mates may attend more strongly to different components of men's voices because of the different types of information these vocal parameters provide.

Full Text Available Cardiovascular disease (CVD is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI, and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.

Background/ Aims: Despite the existence of an easy tool to diagnose biliary tract disease as an etiology for acute pancreatitis (AP), the sensitivity of abdominal ultrasound is around 80%, which can be even lower in certain conditions. We have retrospectively reviewed data of 146 patients admitted for acute pancreatitis between 1999 and 2013. Bivariate analysis for clinical and biochemical variables was performed with respect to etiology of AP (biliary versus non-biliary). Multivariate analysis was performed by using binary logistic regression. There were 87 males (59.6%) and 59 females (40.4%), with a median age of 51. The etiology of acute pancreatitis was biliary in 71 patients (48.6%). Bivariate analysis found the following as significant association (p=0.001) with biliary pancreatitis: older age, female gender, and elevated AST, ALT. A binary logistic regression analysis identified as predictor factors for biliary etiology of acute pancreatitis: age OR = 1.031 (95% CI 1.004 - 1.059, p = 0.024), sex (female) OR = 2.34 (95% CI 1.022 - 5.359, p = 0.044) and ALT OR = 1.004 (95% CI 1.001 - 1.007, p =0.004). The two clinical scores included the three variables (A.S.ALT scores) in categorical format were generated and then checked with the ROC curves (areas under curve are 0.768 and 0.778). Age, female gender, and elevated ALT can help identifying cases with biliary etiology of acute pancreatitis.

Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species. PMID:29346559

Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

Full Text Available The present work had as objective to establish mathematical models that allow the prognosis of the rheological parameters of the lateritic pulp at concentrations of solids from 35% to 48%, temperature of the preheated hydromixture superior to 82 ° C and number of mineral between 3 and 16. Four samples of lateritic pulp were used in the study at different process locations. The results allowed defining that the plastic properties of the lateritic pulp in the conditions of this study conform to the Herschel-Bulkley model for real plastics. In addition, they show that for current operating conditions, even for new situations, UPD mathematical models have a greater ability to predict rheological parameters than least squares mathematical models.

According to the increase in the use of radiotherapy to cancer patients, many approaches have been tried to develop new agents for the protection of surrounding normal tissues. However, it is still few applied in the clinic as a radioprotector. We aim to find a representative parameter for radioprotection to easily predict the activity of in vivo experiment from the results of in vitro screening. The polysaccharide extracted from Panax ginseng was used in this study because the immunostimulator has been regarded as one of the radioprotective agent category and was already reported having a promising radioprotective activity through the increase of hematopoietic cells and the production of several cytokines. Mitogenic activity, AK cells activity and nitric oxide production were monitored for the in vitro immunological assay, and endogenous Colony-Forming Unit (e-CFU) was measured as in vivo radioprotective parameter. The immunological activity was increased by the galactose contents of ginseng polysaccharide dependently. The result of this study suggests that mitogenic activity of splenocytes demonstrated a good correlation with in vivo radioprotective effect, and may be used as a representative parameter to screen the candidates for radioprotector.

Full Text Available Collecting evidence suggests that the intercellular infection of Chlamydia pneumoniae in lungs contributes to the etiology of lung cancer. Many proteins of Chlamydia pneumoniae outmanoeuvre the various system of the host. The infection may regulate various factors, which can influence the growth of lung cancer in affected persons. In this in-silico study, we predict potential targeting of Chlamydia pneumoniae proteins in mitochondrial and cytoplasmic comportments of host cell and their possible involvement in growth and development of lung cancer. Various cellular activities are controlled in mitochondria and cytoplasm, where the localization of Chlamydia pneumoniae proteins may alter the normal functioning of host cells. The rationale of this study is to find out and explain the connection between Chlamydia pneumoniae infection and lung cancer. A sum of 183 and 513 proteins were predicted to target in mitochondria and cytoplasm of host cell out of total 1112 proteins of Chlamydia pneumoniae. In particular, many targeted proteins may interfere with normal growth behaviour of host cells, thereby altering the decision of program cell death. Present article provides a potential connection of Chlamydia pneumoniae protein targeting and proposed that various targeted proteins may play crucial role in lung cancer etiology through diverse mechanisms.

Rearranging and reparameterizing a discrete-time nonlinear model with polynomial quotient structure in input, output and parameters (xk = f(Z, p)) leads to a model linear in its (new) parameters. As a result, the parameter estimation problem becomes a so-called errors-in-variables problem for which

Aim To define predictiveparameters of a complicated clinical course after the TIPSS procedure. Blood cultures were drawn prospectively in 41 patients from a central line and from the portal venous blood before stent placement as well as from the central line 20 min after intervention. C-reactive proteine (CRP) (mg/dl) and white blood cell count (WBC,/μl) on the day of TIPSS-procedure (d0), the first (d1) and seven (d7) days after TIPSS were compared in patients with a complicated clinical course (spontaneous bacterial peritonitis, pneumonia, sepsis; group I) to patients without clinical complications (group II) Group I showed a significant increase in CRP (d0: 1.8±1.0; d1: 3.2±1.5; d7: 4.3±3.2), and white blood cell count (d0: 7700±2600; d1: 10800±2800; d7: 7500±1800) on the first day after TIPSS-procedure in comparison to group II (CRP: d0: 1.6±0.6; d1: 1.8±1.0; d7: 1.9±0.6. WBC: d0: 6900±1500; d1: 8000±1600; d7: 7600±1400).Microbiological analysis showed in 12% skin or oral flora in the last sample. The course of CRP and WBC-count during the first week after TIPSS procedure may indicate patients with a potential risk of a complicated clinical course. (orig.) [de

Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

The need for predictions of flow time-series persists at ungauged catchments, motivating the research goals of our study. By means of the Sacramento model, this paper explores the use of parameter transfer within homogeneous regions of similar climate and flow characteristics and makes comparisons with predictions from a priori parameters. We assessed the performance using the Nash-Sutcliffe (NS), bias, mean monthly hydrograph and flow duration curve (FDC). The study was conducted on a large dataset of 73 catchments within the eastern US. Two approaches to the parameter transferability were developed and evaluated; (i) the within homogeneous region parameter transfer using one donor catchment specific to each region, (ii) the parameter transfer disregarding the geographical limits of homogeneous regions, where one donor catchment was common to all regions. Comparisons between both parameter transfers enabled to assess the gain in performance from the parameter regionalization and its respective constraints and limitations. The parameter transfer within homogeneous regions outperformed the a priori parameters and led to a decrease in bias and increase in efficiency reaching a median NS of 0.77 and a NS of 0.85 at individual catchments. The use of FDC revealed the effect of bias on the inaccuracy of prediction from parameter transfer. In one specific region, of mountainous and forested catchments, the prediction accuracy of the parameter transfer was less satisfactory and equivalent to a priori parameters. In this region, the parameter transfer from the outsider catchment provided the best performance; less-biased with smaller uncertainty in medium flow percentiles (40%-60%). The large disparity of energy conditions explained the lack of performance from parameter transfer in this region. Besides, the subsurface stormflow is predominant and there is a likelihood of lateral preferential flow, which according to its specific properties further explained the reduced

We describe two new methods by which rainfall and hence meteorological droughts at any location on the earth may be predicted. The first method is based upon well supported observations that rainfall distribution at a given location during any local sunspot-related temperature/heat cycle is approximately similar to the distribution during another cycle associated with approximately similar sunspot cycle provided that the two temperature/heat cycles involved are immediately preceded by approximately similar sunspot cycles. The second method is based upon the fact that rainfall belts or patterns seem to be closely related to certain spatial and time-dependent temperature/heat patterns in the earth-atmosphere system. Reasonable predictions of these temperature/heat patterns may be made, and hence the associated rainfall patterns or belts may correspondingly be predicted. Specific examples are given to illustrate the two prediction methods. (author). 12 refs, 11 figs, 1 tab

Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

This book develops and analyses computational wear simulations of the total ankle replacement for the stance phase of gait cycle. The emphasis is put on the relevant design parameters. The book presents a model consisting of three components; tibial, bearing and talar representing their physiological functions.

This paper considers the erodible river corridor, which is the area in which the main river channel is free to migrate over a period of time. Due to growing anthropogenic pressure, predicting the corridor width has become increasingly important for the planning of development along rivers. Several

The present work had as objective to establish mathematical models that allow the prognosis of the rheological parameters of the lateritic pulp at concentrations of solids from 35% to 48%, temperature of the preheated hydromixture superior to 82 ° C and number of mineral between 3 and 16. Four samples of lateritic pulp were used in the study at different process locations. The results allowed defining that the plastic properties of the lateritic pulp in the conditions of this study conform to...

Low mean fundamental frequency (F 0) in men’s voices has been found to positively influence perceptions of dominance by men and attractiveness by women using standardized speech. Using natural speech obtained during an ecologically valid social interaction, we examined relationships between multiple vocal parameters and dominance and attractiveness judgments. Male voices from an unscripted dating game were judged by men for physical and social dominance and by women in fert...

Needle aspiration biopsy is commonly employed in the evaluation of thyroid nodules. Unfortunately, the cytologic finding of a 'follicular neoplasm' does not distinguish between a thyroid adenoma and a follicular cancer. The purpose of this study was to identify clinical parameters that characterize patients with an increased risk of having a thyroid follicular cancer who preoperatively have a 'follicular neoplasm' identified by needle aspiration biopsy. A total of 395 patients initially treated at Vancouver General Hospital and the British Columbia Cancer Agency between the years of 1965 and 1985 were identified and their data were entered into a computer database. Patients with thyroid adenomas were compared to patients with follicular cancer using the chi-square test and Student's t-test. Statistically significant parameters that distinguished patients at risk of having a thyroid cancer (p less than 0.05) included age greater than 50 years, nodule size greater than 3 cm, and a history of neck irradiation. Sex, family history of goiter or neoplasm, alcohol and tobacco use, and use of exogenous estrogen were not significant parameters. Patients can be identified preoperatively to be at an increased risk of having a follicular cancer and accordingly appropriate surgical resection can be planned

fatty acids, protein fractions and coagulation properties from Fourier transform infrared measurements. This thesis shows how such predictions are trapped in a cage of covariance with major milk constituents like total fat and protein content. The prediction models for detailed milk composition...... are not based on causal relationships and this may seriously compromise calibration robustness. It is not recommended to implement indirect models for detailed milk composition in milk recording or breeding programs as such model are providing information on, for example, total protein rather than the specific...... protein fractions. If Fourier transform infrared based models on detailed milk composition are to be implemented in, for example, breeding programs it is recommended to decompose, for example, the individual fatty acids into functional groups, such as methyl, methylene, olefinic and carboxylic groups...

This paper presents evidence that model prediction uncertainty does not necessarily rise with parameter dimensionality (the number of parameters). Here by prediction we mean future simulation of a variable of interest conditioned on certain future values of input variables. We utilize a relationship

The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

The approach to accelerated test parameters evaluation is presented in order to predict CMOS IC total dose behavior in variable dose-rate environment. The technique is based on the analytical model of MOSFET parameters total dose degradation. The simple way to estimate model parameter is proposed using IC's input-output MOSFET radiation test results. (authors)

In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are…

Low streamflow as consequence of a drought event affects numerous aspects of life. Economic sectors that may be impacted by drought are, e.g. power production, agriculture, tourism and water quality management. Numerical models have increasingly been used to forecast low-flow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the low-flow indices duration, severity and magnitude, with a forecast lead-time of one month, to assess their potential usefulness for predictions. The ECMWF VarEPS 5 member reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification shows that, compared to peak flow, probabilistic low-flow forecasts are skillful for longer lead-times, low-flow index forecasts could also be beneficially included in a decision-making process. The results suggest monthly runoff forecasts are useful for accessing the risk of hydrological droughts.

Human insulin exists in different association states, from monomer to hexamer, depending on the conditions. In the presence of zinc the "normal" state is a hexamer. The structural properties of 20 variants of human insulin were studied by near-UV circular dichroism, fluorescence spectroscopy, and small-angle X-ray scattering (SAXS). The mutants showed different degrees of association (monomer, dimers, tetramers, and hexamers) at neutral pH. A correlation was shown between the accessibility of tyrosines to acrylamide quenching and the degree of association of the insulin mutants. The near-UV CD spectra of the insulins were affected by protein association and by mutation-induced structural perturbations. However, the shape and intensity of difference CD spectra, obtained by subtraction of the spectra measured in 20% acetic acid (where all insulin species were monomeric) from the corresponding spectra measured at neutral pH, correlate well with the degree of insulin association. In fact, the near-UV CD difference spectra for monomeric, dimeric, tetrameric, and hexameric insulin are very distinctive, both in terms of intensity and shape. The results show that the spectral properties of the insulins reflect their state of association, and can be used to predict their oligomeric state. Copyright 2003 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 92:847-858, 2003

Biaxial low cycle fatigue test was carried out to predict fatigue life under combined axial-torsional loading condition which is that of in-phase and out-of-phase for CF8M cast stainless steels. Fatemi Socie(FS) parameter which is based on critical plane approach is not only one of methods but also the best method that can predict fatigue life under biaxial loading condition. But the result showed that, biaxial fatigue life prediction by using FS parameter with several different parameters for the CF8M cast stainless steels is not conservative but best results. So in this present research, we proposed new fatigue life predictionparameter considering effective shear stress instead of FS parameter which considers the maximum normal stress acting on maximum shear strain and its effectiveness was verified

The predicted model on the bubble departure diameter in a narrow channel is built by analysis of forces acting on the bubble, and effects of bubble interface parameters such as the bubble inclination angle, upstream contact angle, downstream contact angle and bubble contact diameter on predicted bubble departure diameters in a narrow channel are analysed by comparing with the visual experimental data. Based on the above results, the bubble interface parameters as the input parameters used to obtain the bubble departure diameter in a narrow channel are assured, and the bubble departure diameters in a narrow channel are predicted by solving the force equation. The predicted bubble departure diameters are verified by the 58 bubble departure diameters obtained from the vertical and inclined visual experiment, and the predicted results agree with the experimental results. The different forces acting on the bubble are obtained and the effect of thermal parameters in this experiment on bubble departure diameters is analysed. (authors)

Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities....... Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse. However, it may be possible to estimate...... a subset of model parameters reducing the complexity of the problem. In this study, we compare three methods that allow identification of parameter subsets that can be estimated given a model and a set of data. These methods will be used to estimate patient specific parameters in a model predicting...

Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) were...... varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod....... PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised...

In this study, the prediction power of the item characteristics based on the experts' predictions on conditions try-out practices cannot be applied was examined for item characteristics computed depending on classical test theory and two-parameters logistic model of latent trait theory. The study was carried out on 9914 randomly selected students…

Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

Full Text Available Solar flare prediction has become a forefront topic in contemporary solar physics, with numerous published methods relying on numerous predictiveparameters, that can even be divided into parameter classes. Attempting further insight, we focus on two popular classes of flare-predictiveparameters, namely multiscale (i.e., fractal and multifractal and proxy (i.e., morphological parameters, and we complement our analysis with a study of the predictive capability of fundamental physical parameters (i.e., magnetic free energy and relative magnetic helicity. Rather than applying the studied parameters to a comprehensive statistical sample of flaring and non-flaring active regions, that was the subject of our previous studies, the novelty of this work is their application to an exceptionally long and high-cadence time series of the intensely eruptive National Oceanic and Atmospheric Administration (NOAA active region (AR 11158, observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Aiming for a detailed study of the temporal evolution of each parameter, we seek distinctive patterns that could be associated with the four largest flares in the AR in the course of its five-day observing interval. We find that proxy parameters only tend to show preflare impulses that are practical enough to warrant subsequent investigation with sufficient statistics. Combining these findings with previous results, we conclude that: (i carefully constructed, physically intuitive proxy parameters may be our best asset toward an efficient future flare-forecasting; and (ii the time series of promising parameters may be as important as their instantaneous values. Value-based prediction is the only approach followed so far. Our results call for novel signal and/or image processing techniques to efficiently utilize combined amplitude and temporal-profile information to optimize the inferred solar-flare probabilities.

A computer code (MIGSTEM-FIT) has been developed to determine the predictionparameters, retardation factor, water flow velocity, dispersion coefficient, etc., of radionuclide migration in soil layer from the concentration distribution of radionuclide in soil layer or in effluent. In this code, the solution of the predicting equation for radionuclide migration is compared with the concentration distribution measured, and the most adequate values of parameter can be determined by the flexible tolerance method. The validity of finite differential method, which was one of the method to solve the predicting equation, was confirmed by comparison with the analytical solution, and also the validity of fitting method was confirmed by the fitting of the concentration distribution calculated from known parameters. From the examination about the error, it was found that the error of the parameter obtained by using this code was smaller than that of the concentration distribution measured. (author)

An approach for a prediction of "6"3Ni-based betavoltaic battery output parameters is described. It consists of multilayer Monte Carlo simulation to obtain the depth dependence of excess carrier generation rate inside the semiconductor converter, a determination of collection probability based on the electron beam induced current measurements, a calculation of current induced in the semiconductor converter by beta-radiation, and SEM measurements of output parameters using the calculated induced current value. Such approach allows to predict the betavoltaic battery parameters and optimize the converter design for any real semiconductor structure and any thickness and specific activity of beta-radiation source. - Highlights: • New procedure for betavoltaic battery output parametersprediction is described. • A depth dependence of beta particle energy deposition for Si and SiC is calculated. • Electron trajectories are assumed isotropic and uniformly started under simulation.

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One...... Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear...... regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate...

Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information.

Full Text Available Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information.

BACKGROUND: The etiology of inguinal hernias remains uncertain even though the lifetime risk of developing an inguinal hernia is 27% for men and 3% for women. The aim was to summarize the evidence on hernia etiology, with focus on differences between lateral and medial hernias. RESULTS: Lateral a...

Full Text Available As a major step surface mount technology, reflow process is the key factor affecting the quality of the final product. The setting parameters and characteristic value of temperature curve shows a nonlinear relationship. So parameter impacts on characteristic values are analyzed and the parameters adjustment process based on orthogonal experiment is proposed in the paper. First, setting parameters are determined and the orthogonal test is designed according to production conditions. Then each characteristic value for temperature profile is calculated. Further, multi-index orthogonal experiment is analyzed for acquiring the setting parameters which impacts the PCBA product quality greater. Finally, reliability prediction is carried out considering the main influencing parameters for providing a theoretical basis of parameters adjustment and product quality evaluation in engineering process.

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event. PMID:23202231

Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.

Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new

A total of 386 critical flow data points from 19 runs of 27 runs in the Marviken Test were selected and compared with the predictions by the correlations based on the critical flow scaling parameters. The results show that the critical mass flux in the very large diameter pipe can be also characterized by two scaling parameters such as discharge coefficient and dimensionless subcooling (C{sub d,ref} and {Delta}{Tau}{sup *} {sub sub}). The agreement between the measured data and the predictions are excellent. 8 refs., 8 figs. 1 tab. (Author)

A total of 386 critical flow data points from 19 runs of 27 runs in the Marviken Test were selected and compared with the predictions by the correlations based on the critical flow scaling parameters. The results show that the critical mass flux in the very large diameter pipe can be also characterized by two scaling parameters such as discharge coefficient and dimensionless subcooling (C{sub d,ref} and {Delta}{Tau}{sup *} {sub sub}). The agreement between the measured data and the predictions are excellent. 8 refs., 8 figs. 1 tab. (Author)

Full Text Available Introduction: The value of monitoring physiological parameters to predict chronic obstructive pulmonary disease (COPD exacerbations is controversial. A few studies have suggested benefit from domiciliary monitoring of vital signs, and/or lung function but there is no existing systematic review. Objectives: To conduct a systematic review of the effectiveness of monitoring physiological parameters to predict COPD exacerbation. Methods: An electronic systematic search compliant with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines was conducted. The search was updated to April 6, 2016. Five databases were examined: Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (Medline, Excerpta Medica dataBASE (Embase, Allied and Complementary Medicine Database (AMED, Cumulative Index of Nursing and Allied Health Literature (CINAHL and the Cochrane clinical trials database. Results: Sixteen articles met the pre-specified inclusion criteria. Fifteen of these articules reported positive results in predicting COPD exacerbation via monitoring of physiological parameters. Nine studies showed a reduction in peripheral oxygen saturation (SpO2% prior to exacerbation onset. Three studies for peak flow, and two studies for respiratory rate reported a significant variation prior to or at exacerbation onset. A particular challenge is accounting for baseline heterogeneity in parameters between patients. Conclusion: There is currently insufficient information on how physiological parameters vary prior to exacerbation to support routine domiciliary monitoring for the prediction of exacerbations in COPD. However, the method remains promising.

A new code viz., Linear Output Thermodynamic User-friendly Software for Energetic Systems (LOTUSES) developed during this work predicts the theoretical performance parameters such as density, detonation factor, velocity of detonation, detonation pressure and thermodynamic properties such as heat of detonation, heat of explosion, volume of explosion gaseous products. The same code also assists in the prediction of possible explosive decomposition products after explosion and power index. The developed code has been validated by calculating the parameters of standard explosives such as TNT, PETN, RDX, and HMX. Theoretically predicated parameters are accurate to the order of ±5% deviation. To the best of our knowledge, no such code is reported in literature which can predict a wide range of characteristics of known/unknown explosives with minimum input parameters. The code can be used to obtain thermochemical and performance parameters of high energy materials (HEMs) with reasonable accuracy. The code has been developed in Visual Basic having enhanced windows environment, and thereby advantages over the conventional codes, written in Fortran. The theoretically predicted HEMs performance can be directly printed as well as stored in text (.txt) or HTML (.htm) or Microsoft Word (.doc) or Adobe Acrobat (.pdf) format in the hard disk. The output can also be copied into the Random Access Memory as clipboard text which can be imported/pasted in other software as in the case of other codes

The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates

This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies ...

Seawater pollution problems are gaining interest world wide because of their health impacts and other environmental issues. Intense human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. This paper is concerned with the use of ANNs (Artificial Neural Networks) MLP ( Multilayer Perceptron) model for the prediction of pH and EC (Electrical Conductivity) in water quality parameters along Gaza city coast. MLP neural networks are trained and developed with reference to three major oceanographic parameters (water temperature, wind speed and turbidity) to predict the values of pH and EC; these parameters are considered as inputs of the neural network. The data collected comprised of four years and collected from nine locations along Gaza coastline. Results show that the model has high capability and accuracy in predicting both parameters. The network performance has been validated with different data sets and the results show satisfactory performance. Results of the developed model have been compared with multiple regression statistical models and found that MLP predictions are slightly better than the conventional methods. Prediction results prove that the proposed approach is suitable for modeling the water quality in the Mediterranean Sea along Gaza. (author)

Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

Quantification of uncertainty in predictions of nuclear waste repository performance is a requirement of Nuclear Regulatory Commission regulations governing the licensing of proposed geologic repositories for high-level radioactive waste disposal. One of the major uncertainties in these predictions is in estimating the ground-water travel time of radionuclides migrating from the repository to the accessible environment. The cause of much of this uncertainty has been attributed to a lack of knowledge about the hydrogeologic properties that control the movement of radionuclides through the aquifers. A major reason for this lack of knowledge is the paucity of data that is typically available for characterizing complex ground-water flow systems. Because of this, considerable effort has been put into developing parameter estimation techniques that infer property values in regions where no measurements exist. Currently, no single technique has been shown to be superior or even consistently conservative with respect to predictions of ground-water travel time. This work was undertaken to compare a number of parameter estimation techniques and to evaluate how differences in the parameter estimates and the estimation errors are reflected in the behavior of the flow model predictions. That is, we wished to determine to what degree uncertainties in flow model predictions may be affected simply by the choice of parameter estimation technique used. 3 refs., 2 figs

Full Text Available Grey prediction models have become common methods which are widely employed to solve the problems with “small examples and poor information.” However, modeling objects of existing grey prediction models are limited to the homogenous data sequences which only contain the same data type. This paper studies the methodology of building prediction models of interval grey numbers that are grey heterogeneous data sequence, with a real parameter. Firstly, the position of the real parameter in an interval grey number sequence is discussed, and the real number is expanded into an interval grey number by adopting the method of grey generation. On this basis, a prediction model of interval grey number with a real parameter is deduced and built. Finally, this novel model is successfully applied to forecast the concentration of organic pollutant DDT in the atmosphere. The analysis and research results in this paper extend the object of grey prediction from homogenous data sequence to grey heterogeneous data sequence. Those research findings are of positive significance in terms of enriching and improving the theory system of grey prediction models.

Highlights: • ANFIS is used to select the most relevant variables for dew point temperature prediction. • Two cities from the central and south central parts of Iran are selected as case studies. • Influence of 5 parameters on dew point temperature is evaluated. • Appropriate selection of input variables has a notable effect on prediction. • Considering the most relevant combination of 2 parameters would be more suitable. - Abstract: In this research work, for the first time, the adaptive neuro fuzzy inference system (ANFIS) is employed to propose an approach for identifying the most significant parameters for prediction of daily dew point temperature (T_d_e_w). The ANFIS process for variable selection is implemented, which includes a number of ways to recognize the parameters offering favorable predictions. According to the physical factors influencing the dew formation, 8 variables of daily minimum, maximum and average air temperatures (T_m_i_n, T_m_a_x and T_a_v_g), relative humidity (R_h), atmospheric pressure (P), water vapor pressure (V_P), sunshine hour (n) and horizontal global solar radiation (H) are considered to investigate their effects on T_d_e_w. The used data include 7 years daily measured data of two Iranian cities located in the central and south central parts of the country. The results indicate that despite climate difference between the considered case studies, for both stations, V_P is the most influential variable while R_h is the least relevant element. Furthermore, the combination of T_m_i_n and V_P is recognized as the most influential set to predict T_d_e_w. The conducted examinations show that there is a remarkable difference between the errors achieved for most and less relevant input parameters, which highlights the importance of appropriate selection of input parameters. The use of more than two inputs may not be advisable and appropriate; thus, considering the most relevant combination of 2 parameters would be more suitable

This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

We examined the accuracy of several commonly used soil hydraulic functions and associated parameters for predicting observed soil moisture data. We used six combined methods formed by three commonly used soil hydraulic functions – i.e., Brooks and Corey (1964) (BC), Campbell (19...

This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal

Full Text Available As one of fracture critical components of an aircraft engine, accurate life prediction of a turbine blade to disk attachment is significant for ensuring the engine structural integrity and reliability. Fatigue failure of a turbine blade is often caused under multiaxial cyclic loadings at high temperatures. In this paper, considering different failure types, a new energy-critical plane damage parameter is proposed for multiaxial fatigue life prediction, and no extra fitted material constants will be needed for practical applications. Moreover, three multiaxial models with maximum damage parameters on the critical plane are evaluated under tension-compression and tension-torsion loadings. Experimental data of GH4169 under proportional and non-proportional fatigue loadings and a case study of a turbine disk-blade contact system are introduced for model validation. Results show that model predictions by Wang-Brown (WB and Fatemi-Socie (FS models with maximum damage parameters are conservative and acceptable. For the turbine disk-blade contact system, both of the proposed damage parameters and Smith-Watson-Topper (SWT model show reasonably acceptable correlations with its field number of flight cycles. However, life estimations of the turbine blade reveal that the definition of the maximum damage parameter is not reasonable for the WB model but effective for both the FS and SWT models.

This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

1. The aim of the present study was to evaluate the usefulness of chimeric mice with humanised liver (PXB mice) for the prediction of clearance (CL t ) and volume of distribution at steady state (Vd ss ), in comparison with monkeys, which have been reported as a reliable model for human pharmacokinetics (PK) prediction, and with rats, as a conventional PK model. 2. CL t and Vd ss values in PXB mice, monkeys and rats were determined following intravenous administration of 30 compounds known to be mainly eliminated in humans via the hepatic metabolism by various drug-metabolising enzymes. Using single-species allometric scaling, human CL t and Vd ss values were predicted from the three animal models. 3. Predicted CL t values from PXB mice exhibited the highest predictability: 25 for PXB mice, 21 for monkeys and 14 for rats were predicted within a three-fold range of actual values among 30 compounds. For predicted human Vd ss values, the number of compounds falling within a three-fold range was 23 for PXB mice, 24 for monkeys, and 16 for rats among 29 compounds. PXB mice indicated a higher predictability for CL t and Vd ss values than the other animal models. 4. These results demonstrate the utility of PXB mice in predicting human PK parameters.

The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Nina events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

BACKGROUND: The etiology of inguinal hernias remains uncertain even though the lifetime risk of developing an inguinal hernia is 27% for men and 3% for women. The aim was to summarize the evidence on hernia etiology, with focus on differences between lateral and medial hernias. RESULTS: Lateral...... and medial hernias seem to have common as well as different etiologies. A patent processus vaginalis and increased cumulative mechanical exposure are risk factors for lateral hernias. Patients with medial hernias seem to have a more profoundly altered connective tissue architecture and homeostasis compared...... mechanisms why processus vaginalis fails to obliterate in certain patients should also be clarified. Not all patients with a patent processus vaginalis develop a lateral hernia, but increased intraabdominal pressure appears to be a contributing factor....

The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Statistical analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation failures of the Parallel Ocean Program (POP2). About 8.5% of our POP2 runs failed for numerical reasons at certain combinations of parameter values. We apply support vector machine (SVM) classification from the fields of pattern recognition and machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. The SVM classifiers readily predict POP2 failures in an independent validation ensemble, and are subsequently used to determine the causes of the failures via a global sensitivity analysis. Four parameters related to ocean mixing and viscosity are identified as the major sources of POP2 failures. Our method can be used to improve the robustness of complex scientific models to parameter perturbations and to better steer UQ ensembles. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).

Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.

Full Text Available In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.

Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure...... is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne...... electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential...

Full Text Available It would be important to predict type 2 diabetes mellitus (T2DM and diabetic nephropathy (DN. This study was aimed at evaluating the predicting significance of hemostatic parameters for T2DM and DN. Plasma coagulation and hematologic parameters before treatment were measured in 297 T2DM patients. The risk factors and their predicting power were evaluated. T2DM patients without complications exhibited significantly different activated partial thromboplastin time (aPTT, platelet (PLT, and D-dimer (D-D levels compared with controls (P<0.01. Fibrinogen (FIB, PLT, and D-D increased in DN patients compared with those without complications (P<0.001. Both aPTT and PLT were the independent risk factors for T2DM (OR: 1.320 and 1.211, P<0.01, resp., and FIB and PLT were the independent risk factors for DN (OR: 1.611 and 1.194, P<0.01, resp.. The area under ROC curve (AUC of aPTT and PLT was 0.592 and 0.647, respectively, with low sensitivity in predicting T2DM. AUC of FIB was 0.874 with high sensitivity (85% and specificity (76% for DN, and that of PLT was 0.564, with sensitivity (60% and specificity (89% based on the cutoff values of 3.15 g/L and 245 × 109/L, respectively. This study suggests that hemostatic parameters have a low predicting value for T2DM, whereas fibrinogen is a powerful predictor for DN.

The best estimate plus uncertainty approach (BEAU) requires the use of extensive resources and therefore it is usually applied for cases in which the available safety margin obtained with a conservative methodology can be questioned. Outside the BEAU methodology, there is not a clear approach on how to deal with the issue of considering the uncertainties resulting from prediction errors in the safety analyses performed for licensing submissions. However, the regulatory document RD-310 mentions that the analysis method shall account for uncertainties in the analysis data and models. A possible approach is presented, that is simple and reasonable, representing just the author's views, to take into account the impact of prediction errors and other uncertainties when performing safety analysis in line with regulatory requirements. The approach proposes taking into account the prediction error of relevant parameters. Relevant parameters would be those plant parameters that are surveyed and are used to initiate the action of a mitigating system or those that are representative of the most challenging phenomena for the integrity of a fission barrier. Examples of the application of the methodology are presented involving a comparison between the results with the new approach and a best estimate calculation during the blowdown phase for two small breaks in a generic CANDU 6 station. The calculations are performed with the CATHENA computer code. (author)

We investigated Doppler ultrasonographic (US) parameters of patients with acute stroke to predict the cerebral vascular reserve (CVR) measured by SPECT. We reviewed the flow velocity and cross-sectional area of the circular vessel at the common, external, and internal carotid arteries (ICA) and the vertebral arteries (VA) in 109 acute stroke patients who underwent SPECT. Flow volume (FV) of each artery was calculated as the product of the angle-corrected time averaged flow velocity and cross-sectional area of the circular vessel. Total cerebral FV (TCBFV) was determined as the sum of the FVs of the right and left ICA and VA. We compared the Doppler US parameters between 44 cases of preserved and 65 cases of impaired CVR. In the preserved CVR group, ICA FV, anterior circulating FV (ACFV) and TCBFV were higher than in the impaired CVR group (p < 0.05, independent t-test). In the impaired CVR group, the ROC curves showed ACFV and TCBFV were suitable parameters to predict CVR (p < 0.05). Doppler US was helpful for understanding the hemodynamic state of acute stroke. FV measurement by Doppler US was useful for predicting CVR

The article proposes a forecasting method that allows, based on the given values of entropy and error level of the first and second kind, to determine the allowable time for forecasting the development of the characteristic parameters of a complex information system. The main feature of the method under consideration is the determination of changes in the characteristic parameters of the development of the information system in the form of the magnitude of the increment in the ratios of its entropy. When a predetermined value of the prediction error ratio is reached, that is, the entropy of the system, the characteristic parameters of the system and the depth of the prediction in time are estimated. The resulting values of the characteristics and will be optimal, since at that moment the system possessed the best ratio of entropy as a measure of the degree of organization and orderliness of the structure of the system. To construct a method for estimating the depth of prediction, it is expedient to use the maximum principle of the value of entropy.

Full Text Available Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD. The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.

The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

With the depletion of fossil fuel resources and the potential consequences of climate change due to fossil fuel use, much effort has been put into the search for alternative fuels for transportation. Although there are several potential alternative fuels, which have low impact on the environment, none of these fuels have the ability to be used as the sole 'fuel of the future'. One fuel which is likely to become a part of the over all solution to the transportation fuel dilemma is hydrogen. In this paper, The Toyota Corolla four cylinder, 1.8 l engine running on petrol is systematically converted to run on hydrogen. Several ancillary instruments for measuring various engine operating parameters and emissions are fitted to appraise the performance of the hydrogen car. The effect of hydrogen as a fuel compares with gasoline on engine operating parameters and effect of engine operating parameters on emission characteristics is discussed. Based on the experimental setup, a suite of neural network models were tested to accurately predict the effect of major engine operating conditions on the hydrogen car emissions. Predictions were found to be ±4% to the experimental values. This work provided better understanding of the effect of engine process parameters on emissions. (author)

A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

Purpose: With dose escalation and increasing use of concurrent chemoradiotherapy, radiation esophagitis (RE) remains a common treatment-limiting acute side effect in the treatment of thoracic malignancies. The advent of 3DCT planning has enabled investigators to study esophageal dose-volume histogram (DVH) parameters as predictors of RE. The purpose of this study was to assess published dosimetric parameters and toxicity data systematically in order to define reproducible predictors of RE, both for potential clinical use, and to provide recommendations for future research in the field. Materials and methods: We performed a systematic literature review of published studies addressing RE in the treatment of lung cancer and thymoma. Our search strategy included a variety of electronic medical databases, textbooks and bibliographies. Both prospective and retrospective clinical studies were included. Information relating to the relationship among measured dosimetric parameters, patient demographics, tumor characteristics, chemotherapy and RE was extracted and analyzed. Results: Eighteen published studies were suitable for analysis. Eleven of these assessed acute RE, while the remainder assessed both acute and chronic RE together. Heterogeneity of esophageal contouring practices, individual differences in information reporting and variability of RE outcome definitions were assessed. Well-described clinical and logistic modeling directly related V 35Gy , V 60Gy and SA 55Gy to clinically significant RE. Conclusions: Several reproducible dosimetric parameters exist in the literature, and these may be potentially relevant in the prediction of RE in the radiotherapy of thoracic malignancies. Further clarification of the predictive relationship between such standardized dosimetric parameters and observed RE outcomes is essential to develop efficient radiation treatment planning in locally advanced NSCLC in the modern concurrent chemotherapy and image-guided IMRT era.

This study was an evaluation of the significance of each parameter through aimed at pregnant women subjected to screening test(integrated test) in predicting risk of Down syndrome. We retrospectively analysed the correlation of risk of Down's syndrome with Nuchal Translucency(NT) images measured by ultrasound, Pregnancy Associated Plasma Protein A(PAPP-A), alpha-fetoprotein(AFP), unconjugated estriol(uE3), human chorionic gonadotrophin(hCG) and Inhibin A by maternal serum. As a result, a significant correlation with NT, uE3, hCG, Inhibin A is revealed with Down's syndrome risk(Pparameters to predict Down's syndrome in the integrated test. If we make up for the weakness based on the cut-off value of parameters they will be able to be used as an independent indicator in the risk of Down's syndrome screening.

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model η(θ), where θ denotes the uncertain, best input setting. Hence the statistical model is of the form y=η(θ)+ϵ, where ϵ accounts for measurement, and possibly other, error sources. When nonlinearity is present in η(⋅), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model η(⋅). This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. We also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory. (paper)

This study was an evaluation of the significance of each parameter through aimed at pregnant women subjected to screening test(integrated test) in predicting risk of Down syndrome. We retrospectively analysed the correlation of risk of Down's syndrome with Nuchal Translucency(NT) images measured by ultrasound, Pregnancy Associated Plasma Protein A(PAPP-A), alpha-fetoprotein(AFP), unconjugated estriol(uE3), human chorionic gonadotrophin(hCG) and Inhibin A by maternal serum. As a result, a significant correlation with NT, uE3, hCG, Inhibin A is revealed with Down's syndrome risk(Pparameters to predict Down's syndrome in the integrated test. If we make up for the weakness based on the cut-off value of parameters they will be able to be used as an independent indicator in the risk of Down's syndrome screening

The laser assisted direct write (LADW) method can be used to generate electrical circuitry on a substrate by depositing metallic ink and curing the ink thermally by a laser. Laser curing has emerged over recent years as a novel yet efficient alternative to oven curing. This method can be used in situ, over complicated 3D contours of large parts (e.g. aircraft wings) and selectively cure over heat sensitive substrates, with little or no thermal damage. In previous studies, empirical methods have been used to generate processing windows for this technique, relating to the several interdependent processing parameters on which the curing quality and efficiency strongly depend. Incorrect parameters can result in a track that is cured in some areas and uncured in others, or in damaged substrates. This paper addresses the strong need for a quantitative model which can systematically output the processing conditions for a given combination of ink, substrate and laser source; transforming the LADW technique from a purely empirical approach, to a simple, repeatable, mathematically sound, efficient and predictable process. The method comprises a novel and generic finite element model (FEM) that for the first time predicts the evolution of the thermal profile of the ink track during laser curing and thus generates a parametric map which indicates the most suitable combination of parameters for process optimization. Experimental data are compared with simulation results to verify the accuracy of the model.

Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a Monte-Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that

Highlights: • Dynamic parametric interaction in daily dose prediction under uncertainty. • Importance of temporal dynamics associated with the dose. • Different dose experienced by different population cohorts as a function of time. • Relevance of uncertainty reduction in the input parameters shows temporal dynamism. -- Abstract: We study the time dependent interaction between hydrogeological and exposure parameters in daily dose predictions due to exposure of humans to groundwater contamination. Dose predictions are treated stochastically to account for an incomplete hydrogeological and geochemical field characterization, and an incomplete knowledge of the physiological response. We used a nested Monte Carlo framework to account for uncertainty and variability arising from both hydrogeological and exposure variables. Our interest is in the temporal dynamics of the total dose and their effects on parametric uncertainty reduction. We illustrate the approach to a HCH (lindane) pollution problem at the Ebro River, Spain. The temporal distribution of lindane in the river water can have a strong impact in the evaluation of risk. The total dose displays a non-linear effect on different population cohorts, indicating the need to account for population variability. We then expand the concept of Comparative Information Yield Curves developed earlier (see de Barros et al. [29]) to evaluate parametric uncertainty reduction under temporally variable exposure dose. Results show that the importance of parametric uncertainty reduction varies according to the temporal dynamics of the lindane plume. The approach could be used for any chemical to aid decision makers to better allocate resources towards reducing uncertainty.

Full Text Available This study presents the application of artificial neural networks (ANN and least square support vector machine (LS-SVM for prediction of Marshall parameters obtained from Marshall tests for waste polyethylene (PE modified bituminous mixtures. Waste polyethylene in the form of fibres processed from utilized milk packets has been used to modify the bituminous mixes in order to improve their engineering properties. Marshall tests were carried out on mix specimens with variations in polyethylene and bitumen contents. It has been observed that the addition of waste polyethylene results in the improvement of Marshall characteristics such as stability, flow value and air voids, used to evaluate a bituminous mix. The proposed neural network (NN model uses the quantities of ingredients used for preparation of Marshall specimens such as polyethylene, bitumen and aggregate in order to predict the Marshall stability, flow value and air voids obtained from the tests. Out of two techniques used, the NN based model is found to be compact, reliable and predictable when compared with LS-SVM model. A sensitivity analysis has been performed to identify the importance of the parameters considered.

Recent studies have emphasized that the etiology of tendinopathy is not as simple as was once thought. The etiology is likely to be multifactorial. Etiologic factors may include some of the traditional factors such as overuse, inflexibility, and equipment problems; however, other factors need to be considered as well, such as age-related tendon degeneration and biomechanical considerations as outlined in this article. More research is needed to determine the significance of stress-shielding and compression in tendinopathy. If they are confirmed to play a role, this finding may significantly alter our approach in both prevention and in treatment through exercise therapy. The current biomechanical studies indicate that certain joint positions are more likely to place tensile stress on the area of the tendon commonly affected by tendinopathy. These joint positions seem to be different than the traditional positions for stretching exercises used for prevention and rehabilitation of tendinopathic conditions. Incorporation of different joint positions during stretching exercises may exert more uniform, controlled tensile stress on these affected areas of the tendon and avoid stresshielding. These exercises may be able to better maintain the mechanical strength of that region of the tendon and thereby avoid injury. Alternatively, they could more uniformly stress a healing area of the tendon in a controlled manner, and thereby stimulate healing once an injury has occurred. Additional work will have to prove if a change in rehabilitation exercises is more efficacious that current techniques.

A simple parameter estimation method has been developed to determine the dispersion and velocity parameters associated with stream/river transport. The unsteady one dimensional Burgers' equation was chosen as the model equation, and the method has been applied to recent Savannah River dye tracer studies. The computed Savannah River transport coefficients compare favorably with documented values, and the time/concentration curves calculated from these coefficients compare well with the actual tracer data. The coefficients were used as a predictive capability and applied to Savannah River tritium concentration data obtained during the December 1991 accidental tritium discharge from the Savannah River Site. The peak tritium concentration at the intersection of Highway 301 and the Savannah River was underpredicted by only 5% using the coefficients computed from the dye data

To assess the relationship of parameters of obesity in relationship to coronary angiography findings with correlation of epicardial fat (EF) thickness in uppermentioned context. There were 80 patients examined (43 males, 37 postmenopausal females) undergoing elective coronary angiography. We examined the regular obesity parameters - BMI, waist circumference (WC), neck circumference (NC), total body fat (TBF), and visceral fat (VF) using bioimpedance. We assessed the echocardiographically measured EF thickness. We added examination of lipidogram, glycaemia, HOMA-IR (insulin resistance index) and AIP (aterogenic index of plasma). The set was divided into group with coronarographically proved stenosis or stenoses (withCS), and a group without finding of quantifiable stenosis or stenoses (withoutCS). The average thickness of EF in withCS group was 6.3 vs 5.6 mm in group withoutCS (p obesity parameters in assessment of pre-clinical stages of coronary atherosclerosis and prediction of risk of coronary heart disease. In adipose parameters, EF thickness was correlated the most by WC. Risk stratification of coronary artery disease is supplemented by increased HOMA-IR and AIP.

Full Text Available This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5–18 years of age. We performed Pearson’s correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P<0.05. The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5-18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

Highlights: Black-Right-Pointing-Pointer Quality assurance of process parameters in viscose production. Black-Right-Pointing-Pointer Automatic prediction of spin-bath concentrations based on FTNIR spectra. Black-Right-Pointing-Pointer Evolving chemometric models for efficiently handling changing system dynamics over time (no time-intensive re-calibration needed). Black-Right-Pointing-Pointer Significant reduction of huge errors produced by statistical state-of-the-art calibration methods. Black-Right-Pointing-Pointer Sufficient flexibility achieved by gradual forgetting mechanisms. - Abstract: In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H{sub 2}SO{sub 4}, Na{sub 2}SO{sub 4} and Z{sub n}SO{sub 4}. During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi-Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual

Evaluations of selected predictive models and parameters used in the assessment of the environmental transport and dosimetry of radionuclides are summarized. Mator sections of this report include a validation of the Gaussian plume disperson model, comparison of the output of a model for the transport of 131 I from vegetation to milk with field data, validation of a model for the fraction of aerosols intercepted by vegetation, an evaluation of dose conversion factors for 232 Th, an evaluation of considering the effect of age dependency on population dose estimates, and a summary of validation results for hydrologic transport models

Full Text Available The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

Chemical and mechanical degradation play a key role on the lifetime of dental restorative materials. Therefore, prediction of their long-term performance in the oral environment should base on fatigue, rather than inert strength data, as commonly observed in the dental material's field. The objective of the present study was to provide mechanistic fatigue parameters of current dental CAD/CAM materials under cyclic biaxial flexure and assess their suitability in predicting clinical fracture behaviors. Eight CAD/CAM materials, including polycrystalline zirconia (IPS e.max ZirCAD), reinforced glasses (Vitablocs Mark II, IPS Empress CAD), glass-ceramics (IPS e.max CAD, Suprinity PC, Celtra Duo), as well as hybrid materials (Enamic, Lava Ultimate) were evaluated. Rectangular plates (12×12×1.2mm 3 ) with highly polished surfaces were prepared and tested in biaxial cyclic fatigue in water until fracture using the Ball-on-Three-Balls (B3B) test. Cyclic fatigue parameters n and A* were obtained from the lifetime data for each material and further used to build SPT diagrams. The latter were used to compare in-vitro with in-vivo fracture distributions for IPS e.max CAD and IPS Empress CAD. Susceptibility to subcritical crack growth under cyclic loading was observed for all materials, being more severe (n≤20) in lithium-based glass-ceramics and Vitablocs Mark II. Strength degradations of 40% up to 60% were predicted after only 1 year of service. Threshold stress intensity factors (K th ) representing the onset of subcritical crack growth (SCG), were estimated to lie in the range of 0.37-0.44 of K Ic for the lithium-based glass-ceramics and Vitablocs Mark II and between 0.51-0.59 of K Ic for the other materials. Failure distributions associated with mechanistic estimations of strength degradation in-vitro showed to be useful in interpreting failure behavior in-vivo. The parameter K th stood out as a better predictor of clinical performance in detriment to the SCG n

Full Text Available BACKGROUND: Hospital-acquired infections (HAI are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR and validated by Artificial Neural Networks (ANN simultaneously. METHODOLOGY/PRINCIPAL FINDINGS: A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507 to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447. The scoring system also performed extremely well in the internal (AUC: 0.965 and external (AUC: 0.871 validations. CONCLUSIONS: We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction

The multi factorial character of the radiocaesium and radiostrontium soil-to-plan transfer, which depends on the radionuclide level in the soil solution amplified by a plant factor, prevents from establishing univariate relationships between transfer factors and soil and/or plant parameters. The plant factor is inversely proportional to the level of competitive species in the soil solution (Ca and Mg, for radiostrontium, and K and NH 4 for radiocaesium). Radionuclide level in soil solution depends on the radionuclide available fraction and its distribution coefficient. For radiostrontium, this may be obtained from the Cationic Exchange Capacity (CEC), whereas for radiocaesium the Specific Interception Potential should be calculate, both corrected by the concentrations of the competitive species and selectivity coefficients. Therefore, the transfer factor eventually depends on soil solution composition, the available fraction and the number of sorption sites, as well as on the plant factor. For a given plant, a relative sequence of transfer can be set up based solely on soil parameters, since the plant factor is cancelled. This prediction model has been compared with transfer data from experiments with Mediterranean, mineral soils, contaminated with a thermo generated aerosol, and with podzolic and organic soils, contaminated by the Chernobyl fallout. These studies revealed that it was possible to predict a relative scale of transfer for any type of soil, also allowing a scale of soil vulnerability to radiostrontium and radiocaesium contamination to be set up. (Author)

We investigate electrodiagnostic markers to determine which parameters are the best predictors of spontaneous electromyographic (EMG) activity in carpal tunnel syndrome (CTS). We enrolled 229 patients with clinically proven and nerve conduction study (NCS)-proven CTS, as well as 100 normal control subjects. All subjects were evaluated using electrodiagnostic techniques, including median distal sensory latencies (DSLs), sensory nerve action potentials (SNAPs), distal motor latencies (DMLs), compound muscle action potentials (CMAPs), forearm median nerve conduction velocities (FMCVs) and wrist-palm motor conduction velocities (W-P MCVs). All CTS patients underwent EMG examination of the abductor pollicis brevis (APB) muscle, and the presence or absence of spontaneous EMG activities was recorded. Normal limits were determined by calculating the means ± 2 standard deviations from the control data. Associations between parameters from the NCS and EMG findings were investigated. In patients with clinically diagnosed CTS, abnormal median CMAP amplitudes were the best predictors of spontaneous activity during EMG examination (p95% (positive predictive rate >95%). If the median CMAP amplitude was higher than the normal limit (>4.9 mV), the rate of no spontaneous EMG activity was >94% (negative predictive rate >94%). An abnormal SNAP amplitude was the second best predictor of spontaneous EMG activity (p<0.001; OR 4.13; 95% CI 2.16-7.90), and an abnormal FMCV was the third best predictor (p=0.01; OR 2.10; 95% CI 1.20-3.67). No other nerve conduction parameters had significant power to predict spontaneous activity upon EMG examination. The CMAP amplitudes of the APB are the most powerful predictors of the occurrence of spontaneous EMG activity. Low CMAP amplitudes are strongly associated with spontaneous activity, whereas high CMAP amplitude are less associated with spontaneous activity, implying that needle EMG examination should be recommended for the detection of

This work details a methodology to obtain optimal parameter values for a locally-adaptive texture analysis algorithm that extracts mammographic texture features representative of breast parenchymal complexity for predicting falsepositive (FP) recalls from breast cancer screening with digital mammography. The algorithm has two components: (1) adaptive selection of localized regions of interest (ROIs) and (2) Haralick texture feature extraction via Gray- Level Co-Occurrence Matrices (GLCM). The following parameters were systematically varied: mammographic views used, upper limit of the ROI window size used for adaptive ROI selection, GLCM distance offsets, and gray levels (binning) used for feature extraction. Each iteration per parameter set had logistic regression with stepwise feature selection performed on a clinical screening cohort of 474 non-recalled women and 68 FP recalled women; FP recall prediction was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC) and associations between the extracted features and FP recall were assessed via odds ratios (OR). A default instance of mediolateral (MLO) view, upper ROI size limit of 143.36 mm (2048 pixels2), GLCM distance offset combination range of 0.07 to 0.84 mm (1 to 12 pixels) and 16 GLCM gray levels was set. The highest ROC performance value of AUC=0.77 [95% confidence intervals: 0.71-0.83] was obtained at three specific instances: the default instance, upper ROI window equal to 17.92 mm (256 pixels2), and gray levels set to 128. The texture feature of sum average was chosen as a statistically significant (p<0.05) predictor and associated with higher odds of FP recall for 12 out of 14 total instances.

Highlights: • Corrosion resistance and impact strength – predicted by response surface methodology. • Burn off length has highest significance on corrosion resistance. • Friction force is a strong determinant in changing impact strength. • Pareto front points generated by genetic algorithm aid to fix input control variable. • Pareto front will be a trade-off between corrosion resistance and impact strength. - Abstract: Friction welding finds widespread industrial use as a mass production process for joining materials. Friction welding process allows welding of several materials that are extremely difficult to fusion weld. Friction welding process parameters play a significant role in making good quality joints. To produce a good quality joint it is important to set up proper welding process parameters. This can be done by employing optimization techniques. This paper presents a multi objective optimization method for optimizing the process parameters during friction welding process. The proposed method combines the response surface methodology (RSM) with an intelligent optimization algorithm, i.e. genetic algorithm (GA). Corrosion resistance and impact strength of friction welded super duplex stainless steel (SDSS) (UNS S32760) joints were investigated considering three process parameters: friction force (F), upset force (U) and burn off length (B). Mathematical models were developed and the responses were adequately predicted. Direct and interaction effects of process parameters on responses were studied by plotting graphs. Burn off length has high significance on corrosion current followed by upset force and friction force. In the case of impact strength, friction force has high significance followed by upset force and burn off length. Multi objective optimization for maximizing the impact strength and minimizing the corrosion current (maximizing corrosion resistance) was carried out using GA with the RSM model. The optimization procedure resulted in

Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

The control system of a reactor should be able to predict, in real time, the amount of reactivity to be inserted (e.g., by control rod movements and boron injection and dilution) to respond to a given electrical load demand or to undesired, accidental transients. The real-time constraint renders impractical the use of a large, detailed dynamic reactor code. One has, then, to resort to simplified analytical models with lumped effective parameters suitably estimated from the reactor data.The simple and well-known Chernick model for describing the reactor power evolution in the presence of xenon is considered and the feasibility of using genetic algorithms for estimating the effective nuclear parameters involved and the initial nonmeasurable xenon and iodine conditions is investigated. This approach has the advantage of counterbalancing the inherent model simplicity with the periodic reestimation of the effective parameter values pertaining to each reactor on the basis of its recent history. By so doing, other effects, such as burnup, are automatically taken into account

Full Text Available Multimedia communications have experienced an unprecedented growth due mainly to the increase in the content quality and the emergence of smart devices. The demand for these contents is tending towards wireless technologies. However, these transmissions are quite sensitive to network delays. Therefore, ensuring an optimum QoS level becomes of great importance. The IEEE 802.11e amendment was released to address the lack of QoS capabilities in the original IEEE 802.11 standard. Accordingly, the Enhanced Distributed Channel Access (EDCA function was introduced, allowing it to differentiate traffic streams through a group of Medium Access Control (MAC parameters. Although EDCA recommends a default configuration for these parameters, it has been proved that it is not optimum in many scenarios. In this work a dynamic prediction scheme for these parameters is presented. This approach ensures an appropriate traffic differentiation while maintaining compatibility with the stations without QoS support. As the APs are the only devices that use this algorithm, no changes are required to current network cards. The results show improvements in both voice and video transmissions, as well as in the QoS level of the network that the proposal achieves with regard to EDCA.

Full Text Available Finite element models of the hot stamping and cold die quenching process for boron steel sheet were developed using either rigid or elastic tools. The effect of tool elasticity and process parameters on workpiece temperature was investigated. Heat transfer coefficient between blank and tools was modelled as a function of gap and contact pressure. Temperature distribution and thermal history in the blank were predicted, and thickness distribution of the blank was obtained. Tests were carried out and the test results are used for the validation of numerical predictions. The effect of holding load and the size of cooling ducts on temperature distribution during the forming and the cool die quenching process was also studied by using two models. The results show that higher accuracy predictions of blank thickness and temperature distribution during deformation were obtained using the elastic tool model. However, temperature results obtained using the rigid tool model were close to those using the elastic tool model for a range of holding load.

The Burning Mouth Syndrome (BMS) is an oral mucosa pain--with or without inflammatory signs--without any specific lesion. It is mostly observed in women aged 40-60 years. This pain feels like a moderate/severe burning, and it occurs more frequently on the tongue, but it may also be felt at the gingiva, lips and jugal mucosa. It may worsen during the day, during stress and fatigue, when the patient speaks too much, or through eating of spicy/hot foods. The burning can be diminished with cold food, work and leisure. The goal of this review article is to consider possible BMS etiologies and join them in 4 groups to be better studied: local, systemic, emotional and idiopathic causes of pain. Knowing the different diagnoses of this syndrome, we can establish a protocol to manage these patients. Within the local pain group, we must investigate dental, allergic and infectious causes. Concerning systemic causes we need to look for connective tissue diseases, endocrine disorders, neurological diseases, nutritional deficits and salivary glands alterations that result in xerostomia. BMS etiology may be of difficult diagnosis, many times showing more than one cause for oral pain. A detailed interview, general physical examination, oral cavity and oropharynx inspection, and lab exams are essential to avoid a try and error treatment for these patients.

Full Text Available Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in

Forested areas disturbed by access roads produce large amounts of sediment. One method to predict erosion and, hence, manage forest roads is the use of physically based soil erosion models. A perceived advantage of a physically based model is that it can be parameterized at one location and applied at another location with similar soil texture or geological parent...

. Multiple regression model of P. indica abundance on the parameters: temperature, salinity, dissolved oxygen and phosphate-phosphorus could explain more than 85% of the variation in the predicted abundance, while those of 8 species obtained from...

Full Text Available Abstract By the analytical model proposed by Dincer and Dost, the mass transfer parameters (moisture transfer coefficient and moisture diffusivity of shrimp samples were determined. Three sets of drying experiments were performed with three samples of shrimp: without boiling (WB, boiled in salt solution (SB and boiled in salt solution and subjected to liquid smoking process (SBS. The experiments were performed under controlled conditions of drying air at temperature of 60°C and velocity of 1.5 m/s. Experimental dimensionless moisture content data were used to calculate the drying coefficients and lag factors, which were then incorporated into the analytical model for slab and cylinder shapes. The results showed an adequate fit between the experimental data and the values predicted from the correlation. The boiling is the most recommended pretreatment, because provided a shorter drying time, with high values of moisture transfer coefficient and moisture diffusivity.

Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.

Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

To investigate predictors affecting the development of hypothyroidism after radiotherapy for head and neck cancer, focusing on radiation dose-volumetric parameters, and to determine the appropriate radiation dose-volumetric threshold of radiation-induced hypothyroidism. A total of 114 patients with head and neck cancer whose radiotherapy fields included the thyroid gland were analysed. The purpose of the radiotherapy was either definitive (n=81) or post-operative (n=33). Thyroid function was monitored before starting radiotherapy and after completion of radiotherapy at 1 month, 6 months, 1 year and 2 years. A diagnosis of hypothyroidism was based on a thyroid stimulating hormone value greater than the maximum value of laboratory range, regardless of symptoms. In all patients, dose volumetric parameters were analysed. Median follow-up duration was 25 months (range; 6-38). Forty-six percent of the patients were diagnosed as hypothyroidism after a median time of 8 months (range; 1-24). There were no significant differences in the distribution of age, gender, surgery, radiotherapy technique and chemotherapy between the euthyroid group and the hypothyroid group. In univariate analysis, the mean dose and V35-V50 results were significantly associated with hypothyroidism. The V45 is the only variable that independently contributes to the prediction of hypothyroidism in multivariate analysis and V45 of 50% was a threshold value. If V45 was <50%, the cumulative incidence of hypothyroidism at 1 year was 22.8%, whereas the incidence was 56.1% if V45 was ≥50%. (P=0.034). The V45 may predict risk of developing hypothyroidism after radiotherapy for head and neck cancer, and a V45 of 50% can be a useful dose-volumetric threshold of radiation-induced hypothyroidism. (author)

The presence of gastric ectopic mucosa in Meckel's diverticulum is associated with a higher risk of development of complications. The aim of the present study was to investigate which demographic/clinical parameterspredict the presence of gastric heterotopia in Meckel's diverticulum. This was a retrospective cohort study conducted in a single institution (University Hospital Ostrava, Czech republic). All children who underwent laparoscopic/open resection of Meckel's diverticulum within a 20-year study period were included in the study. In total, 88 pediatric patients underwent analysis. The mean age of the children was 4.6 ± 4.73 years; the male-female ratio was approximately 2:1. There were 50 (56.8%) patients with asymptomatic Meckel's diverticulum in our study group. Laparoscopic resection was performed in 24 (27.3%) patients; segmental bowel resection through laparotomy was performed in 13 (14.8%) patients. Gastric heterotopia was found in 39 (44.3%) patients; resection margins of all patients were clear of gastric heterotopia. No correlation was found between the presence of gastric heterotopia and the following parameters: age, gender, maternal age, prematurity, low birth weight, perinatal asphyxia, distance from Bauhin's valve and length of Meckel's diverticulum. The width of the diverticulum base was significantly higher in patients with gastric heterotopia (2.1 ± 0.57 vs. 1.2 ± 0.41 cm; p predictive factor associated with the presence of gastric heterotopia in Meckel's diverticulum. The laparoscopic/open resection of asymptomatic MD with a wide base should therefore be recommended.

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

Hemorheological and glycemic parameters and high density lipoprotein (HDL) cholesterol are used as biomarkers of atherosclerosis and thrombosis. To investigate the association and clinical relevance of erythrocyte sedimentation rate (ESR), fibrinogen, fasting glucose, glycated hemoglobin (HbA1c), and HDL cholesterol in the prediction of major adverse cardiovascular events (MACE) and coronary heart disease (CHD) in an outpatient population. 708 stable patients who visited the outpatient department were enrolled and followed for a mean period of 28.5 months. Patients were divided into two groups, patients without MACE and patients with MACE, which included cardiac death, acute myocardial infarction, newly diagnosed CHD, and cerebral vascular accident. We compared hemorheological and glycemic parameters and lipid profiles between the groups. Patients with MACE had significantly higher ESR, fibrinogen, fasting glucose, and HbA1c, while lower HDL cholesterol compared with patients without MACE. High ESR and fibrinogen and low HDL cholesterol significantly increased the risk of MACE in multivariate regression analysis. In patients with MACE, high fibrinogen and HbA1c levels increased the risk of multivessel CHD. Furthermore, ESR and fibrinogen were significantly positively correlated with HbA1c and negatively correlated with HDL cholesterol, however not correlated with fasting glucose. Hemorheological abnormalities, poor glycemic control, and low HDL cholesterol are correlated with each other and could serve as simple and useful surrogate markers and predictors for MACE and CHD in outpatients

Hemorheological and glycemic parameters and high density lipoprotein (HDL) cholesterol are used as biomarkers of atherosclerosis and thrombosis. To investigate the association and clinical relevance of erythrocyte sedimentation rate (ESR), fibrinogen, fasting glucose, glycated hemoglobin (HbA1c), and HDL cholesterol in the prediction of major adverse cardiovascular events (MACE) and coronary heart disease (CHD) in an outpatient population. 708 stable patients who visited the outpatient department were enrolled and followed for a mean period of 28.5 months. Patients were divided into two groups, patients without MACE and patients with MACE, which included cardiac death, acute myocardial infarction, newly diagnosed CHD, and cerebral vascular accident. We compared hemorheological and glycemic parameters and lipid profiles between the groups. Patients with MACE had significantly higher ESR, fibrinogen, fasting glucose, and HbA1c, while lower HDL cholesterol compared with patients without MACE. High ESR and fibrinogen and low HDL cholesterol significantly increased the risk of MACE in multivariate regression analysis. In patients with MACE, high fibrinogen and HbA1c levels increased the risk of multivessel CHD. Furthermore, ESR and fibrinogen were significantly positively correlated with HbA1c and negatively correlated with HDL cholesterol, however not correlated with fasting glucose. Hemorheological abnormalities, poor glycemic control, and low HDL cholesterol are correlated with each other and could serve as simple and useful surrogate markers and predictors for MACE and CHD in outpatients.

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

The Advanced Radiographic Capability (ARC) laser at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory is the world's most energetic short-pulse laser. It comprises four beamlets, each of substantial energy ( 1.5 kJ), extended short-pulse duration (10-30 ps), and large focal spot (>=50% of energy in 150 µm spot). This allows ARC to achieve proton and light ion acceleration via the Target Normal Sheath Acceleration (TNSA) mechanism, but it is yet unknown how proton beam characteristics scale with ARC-regime laser parameters. As theory has also not yet been validated for laser-generated protons at ARC-regime laser parameters, we attempt to formulate the scaling physics of proton beam characteristics as a function of laser energy, intensity, focal spot size, pulse length, target geometry, etc. through a review of relevant proton acceleration experiments from laser facilities across the world. These predicted scaling laws should then guide target design and future diagnostics for desired proton beam experiments on the NIF ARC. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and funded by the LLNL LDRD program under tracking code 17-ERD-039.

The physicochemical properties of eight hydrocarbon-contaminated soils were used to predict toxicity to earthworms (Eisenia fetida) and plants. The toxicity of these preremediated soils was assessed using earthworm avoidance, survival, and reproduction and seed germination and root growth in four plant species. No-observed-effect and 25% inhibitory concentrations were determined from the earthworm and plant assays. Physical property measurements and metals analyses of the soils were conducted. Hydrocarbon contamination was characterized by total petroleum hydrocarbons, oil and grease, and GC boiling-point distribution. Univariate and multivariate statistical methods were used to examine relationships between physical and chemical properties and biological endpoints. Soil groupings based on physicochemical properties and toxicity from cluster and principal component analyses were generally similar. Correlation analysis identified a number of significant relationships between soil parameters and toxicity that were used in univariate model development. Total petroleum hydrocarbons by gas chromatography and polars were identified as predictors of earthworm avoidance and survival and seed germination, explaining 65 to 75% of the variation in the data. Asphaltenes also explained 83% of the variation in seed germination. Gravimetric total petroleum hydrocarbons explained 40% of the variation in earthworm reproduction, whereas 43% of the variation in plant root growth was explained by asphaltenes. Multivariate one-component partial least squares models, which identified predictors similar to those identified by the univariate models, were also developed for worm avoidance and survival and seed germination and had predictive powers of 42 and 29%, respectively.

To identify parameters of Leishmania infection within a population of infected sand flies that reliably predict subsequent transmission to the mammalian host, we sampled groups of infected flies and compared infection intensity and degree of metacyclogenesis with the frequency of transmission. The percentage of parasites within the midgut that were metacyclic promastigotes had the highest correlation with the frequency of transmission. Meta-analysis of multiple transmission experiments allowed us to establish a percent-metacyclic "cutoff" value that predicted transmission competence. Sand fly infections initiated with variable doses of parasites resulted in correspondingly altered percentages of metacyclic promastigotes, resulting in altered transmission frequency and disease severity. Lastly, alteration of sand fly oviposition status and environmental conditions at the time of transmission also influenced transmission frequency. These observations have implications for transmission of Leishmania by the sand fly vector in both the laboratory and in nature, including how the number of organisms acquired by the sand fly from an infection reservoir may influence the clinical outcome of infection following transmission by bite.

BACKGROUND: Controversy exists about the correlation between liver ultrasonography and serum parameters for evaluating the severity of liver involvement in non-alcoholic fatty liver disease (NAFLD). This study was designed to determine the association between liver ultrasonography staging in NAFLD and serum parameters correlated with disease severity in previous studies; and set optimal cut-off points for those serum parameters correlated with NAFLD staging at ultrasonography, in order to differentiate ultrasonographic groups (USGs). METHODS: This cross-sectional study evaluated outpatients with evidence of NAFLD in ultrasonography referred to a general hospital. Those with positive viral markers, abnormal serum ceruloplasmin or gamma-globulin concentrations were excluded. A radiologist performed the ultrasonography staging and stratiifed the patients into mild, moderate, and severe groups. Fasting serum alanine aminotransferase (ALT), aspartate aminotransferase, alkaline phosphatase, triglyceride (TG), high and low density lipoprotein (HDL, LDL), and cholesterol were checked. RESULTS:Two hundred and forty-ifve patients with a mean age (±standard deviation) of 41.63(±11.46) years were included. There were no signiifcant differences when mean laboratory concentrations were compared between moderate and severe USGs. Therefore, these groups were combined to create revised USGs ("mild"versus"moderate or severe"). There were associations between the revised USGs, and ALT, TG, HDL levels, and diabetes mellitus [odds ratios=2.81 (95%conifdence interval (CI):1.37-5.76), 2.48 (95%CI:1.29-4.78), 0.36 (95%CI:0.18-0.74), and 5.65 (95%CI:2.86-11.16) respectively;all P values CONCLUSIONS: Serum ALT, TG, and HDL concentrations seem to be associated with the staging by liver ultrasonography in NAFLD. They might be used to predict the staging of liver ultrasonography in these patients.

the analytical and statistical methods. It is expected that the neural network can more accurately predict the rolling resistance. In this study, the neural network for experimental data was trained and the relationship among some parameters of velocity, dynamic load and tire pressure and rolling resistance were evaluated. Materials and Methods: The soil bin and single wheel tester of Biosystem Engineering Mechanics Department of Urmia University was used in this study. This soil bin has 24 m length, 2 m width and 1 m depth including a single-wheel tester and the carrier. Tester consists of four horizontal arms and a vertical arm to vertical load. The S-shaped load cells were employed in horizontal arms with a load capacity of 200 kg and another 500 kg in the vertical arm was embedded. The tire used in this study was a general pneumatic tire (Good year 9.5L-14, 6 ply In this study, artificial neural networks were used for optimizing the rolling resistance by 35 neurons, 6 inputs and 1 output choices. Comparison of neural network models according to the mean square error and correlation coefficient was used. In addition, 60% of the data on training, 20% on test and finally 20% of the credits was allocated to the validation and Output parameter of the neural network model has determined the tire rolling resistance. Finally, this study predicts the effects of changing parameters of tire pressure, vertical load and velocity on rolling resistance using a trained neural network. Results and Discussion: Based on obtained error of Levenberg- Marquardt algorithm, neural network with 35 neurons in the hidden layer with sigmoid tangent function and one neuron in the output layer with linear actuator function were selected. The regression coefficient of tested network is 0.92 which seems acceptable, considering the complexity of the studied process. Some of the input parameters to the network are speed, pressure and vertical load which their relationship with the rolling

The aim of this study was to discriminate mole pregnancies and invasive forms among cases with first trimester vaginal bleeding by the utilization of some complete blood count parameters conjunct to sonographic findings and beta human chorionic gonadotropin concentration. Consecutive 257 cases with histopathologically confirmed mole pregnancies and 199 women without mole pregnancy presented with first trimester vaginal bleeding who admitted to Zeynep Kamil Women and Children's Health Training Hospital between January 2012 and January 2016 were included in this cross-sectional study. The serum beta HCG level at presentation, and beta hCG levels at 1st, 2nd and 3rd weeks of postevacuation with some parameters of complete blood count were utilized to discriminate cases with molar pregnancy and cases with invasive mole among first trimester pregnants presented with vaginal bleeding and abnormal sonographic findings. Levels of beta hCG at baseline (AUC = 0.700, p predicting persistency in molar patients. For 8.55 cut-off point for mean platelet volume, sensitivity is 84.6% and specificity is 51.6%. Area under curve for platelet/lymphocyte ratio is 0.683 means that platelet/lymphocyte ratio has additional 18.3% diagnostic value. For 102.25 cut-off point sensitivity is 86.6% and specificity is 46.2. Simple, widely available complete blood count parameters may be used as an adjunct to other risk factors to diagnose molar pregnancies and predict postevacuation trophoblastic disease.

1. Excessive deposition of body fat, especially abdominal fat, is detrimental in chickens and the prevention of excessive fat accumulation is an important problem. The aim of this study was to identify blood biochemical indicators that could be used as criteria to select lean Yellow-feathered chicken lines. 2. Levels of blood biochemical indicators in the fed and fasted states and the abdominal fat traits were measured in 332 Guangxi Yellow chickens. In the fed state, the genetic correlations (r g ) of triglycerides and very low density lipoprotein levels were positive for the abdominal fat traits (0.47 ≤ r g ≤ 0.67), whereas total cholesterol, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) showed higher negative correlations with abdominal fat traits (-0.59 ≤ r g ≤ -0.33). Heritabilities of these blood biochemical parameters were high, varying from 0.26 to 0.60. 3. In the fasted state, HDL-C:LDL-C level was positively correlated with abdominal fat traits (0.35 ≤ r g ≤ 0.38), but triglycerides, total cholesterol, HDL-C, LDL-C, total protein, albumin, aspartate transaminase, uric acid and creatinine levels were negatively correlated with abdominal fat traits (-0.79 ≤ r g ≤ -0.35). The heritabilities of these 10 blood biochemical parameters were high (0.22 ≤ h 2 ≤ 0.59). 4. In the fed state, optimal multiple regression models were constructed to predict abdominal fat traits by using triglycerides and LDL-C. In the fasted state, triglycerides, total cholesterol, HDL-C, LDL-C, total protein, albumin and uric acid could be used to predict abdominal fat content. 5. It was concluded that these models in both nutritional states could be used to predict abdominal fat content in Guangxi Yellow broiler chickens.

We derive an analytical approximation for making quantitative predictions for ecological communities as a function of the mean intensity of the inter-specific competition and the species richness. This method, with only a fraction of the model parameters (carrying capacities and competition coefficients), is able to predict accurately empirical measurements covering a wide variety of taxa (algae, plants, protozoa).

Electronic portal imaging devices (EPIDs) are increasingly used for portal dosimetry applications. In our department, EPIDs are clinically used for two-dimensional (2D) transit dosimetry. Predicted and measured portal dose images are compared to detect dose delivery errors caused for instance by setup errors or organ motion. The aim of this work is to develop a model to predict dose-volume histogram (DVH) changes due to setup errors during breast cancer treatment using 2D transit dosimetry. First, correlations between DVH parameter changes and 2D gamma parameters are investigated for different simulated setup errors, which are described by a binomial logistic regression model. The model calculates the probability that a DVH parameter changes more than a specific tolerance level and uses several gamma evaluation parameters for the planning target volume (PTV) projection in the EPID plane as input. Second, the predictive model is applied to clinically measured portal images. Predicted DVH parameter changes are compared to calculated DVH parameter changes using the measured setup error resulting from a dosimetric registration procedure. Statistical accuracy is investigated by using receiver operating characteristic (ROC) curves and values for the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Changes in the mean PTV dose larger than 5%, and changes in V 90 and V 95 larger than 10% are accurately predicted based on a set of 2D gamma parameters. Most pronounced changes in the three DVH parameters are found for setup errors in the lateral-medial direction. AUC, sensitivity, specificity, and negative predictive values were between 85% and 100% while the positive predictive values were lower but still higher than 54%. Clinical predictive value is decreased due to the occurrence of patient rotations or breast deformations during treatment, but the overall reliability of the predictive model remains high. Based on our

There is consensus in the scientific community about the great need of spatial soil information. Conventional mapping methods are time consuming and involve high costs. Digital soil mapping has emerged as an area in which the soil mapping is optimized by the application of mathematical and statistical approaches, as well as the application of expert knowledge in pedology. In this sense, the objective of the study was to develop a methodology for the spatial prediction of soil classes by using soil spectroscopy methodologies related with fieldwork, spectral data from satellite image and terrain attributes in simultaneous. The studied area is located in São Paulo State, and comprised an area of 473 ha, which was covered by a regular grid (100 x 100 m). In each grid node was collected soil samples at two depths (layers A and B). There were extracted 206 samples from transect sections and submitted to soil analysis (clay, Al2O3, Fe2O3, SiO2 TiO2, and weathering index). The first analog soil class map (ASC-N) contains only soil information regarding from orders to subgroups of the USDA Soil Taxonomy System. The second (ASC-H) map contains some additional information related to some soil attributes like color, ferric levels and base sum. For the elaboration of the digital soil maps the data was divided into three groups: i) Predicted soil attributes of the layer B (related to the soil weathering) which were obtained by using a local soil spectral library; ii) Spectral bands data extracted from a Landsat image; and iii) Terrain parameters. This information was summarized by a principal component analysis (PCA) in each group. Digital soil maps were generated by supervised classification using a maximum likelihood method. The trainee information for this classification was extracted from five toposequences based on the analog soil class maps. The spectral models of weathering soil attributes shown a high predictive performance with low error (R2 0.71 to 0.90). The spatial

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predictparameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. PMID:23673224

Once again, I find Mr. Cooper quote-worthy for his statement, "It is incumbent upon the trial bar not to support the status quo merely because it is in our economic interest. Change is in the wind, and our tort system will be blown away on the winds of change for change's sake unless we participate in correcting deficiencies in the tort system and civil jury trial process." I suggest that we cannot ask for change for our own economic interest, nor can we lay blame exclusively to the other etiologic elements. We must improve those elements within our purview. The prayer of serenity may serve us well: God, grant me the serenity to accept the things I cannot change, the courage to change the things I can, and the wisdom to know the difference. In the game of professional liability litigation as played by the rules extant there are clearly winners and losers. The winners are the legal profession, both plaintiff and defense, and the insurers, who in the face of adversity simply increase premiums or withdraw from the market. The losers are the medical profession, the patients for whom they care and, in the broadest sense, our society as a whole. So as not to close on a note of gloom, one last quote. Lawrence H. Cooke, former Chief Judge of New York State, in remarks to the April 1986 National Symposium on Civil Justice Issues stated, "Our justice systems are beset with very real problems.(ABSTRACT TRUNCATED AT 250 WORDS)

It is a well-established fact that in squamous cell carcinoma cases, the presence of lymph node metastases decreased the 5-year survival rate by 50% and also caused the recurrence of the primary tumor with development of distant metastases. Till date, the predictive factors for occult cervical lymph nodes metastases in cases of tongue squamous cell carcinoma remain inconclusive. Therefore, it is imperative to identify patients who are at the greatest risk for occult cervical metastases. This study was thus performed with the aim to identify various histopathologic parameters of the primary tumor that predict occult nodal metastases. The clinicopathologic features of 56 cases of lateral tongue squamous cell carcinoma with cT1NoMo/cT2NoMo as the stage and without prior radiotherapy or chemotherapy were considered. The surgical excision of primary tumor was followed by elective neck dissection. The glossectomy specimen along with the neck nodes were fixed in formalin and 5 urn thick sections were obtained. The hematoxylin & eosin stained sections were then subjected to microscopic examination. The primary tumor characteristics that were analyzed include tumor grade, invading front, depth of tumor, lymphovascular invasion, perineural invasion and inflammatory response. The nodes were examined for possible metastases using hematoxylin & eosin followed by cytokeratin immunohistochemistry. A total of 12 cases were found with positive occult nodal metastases. On performing univariate analysis, the histopathologic parameters that were found to be statistically significant were lymphovascular invasion (p = 0.004) and perineural invasion (p = 0.003) along with a cut-off depth of infiltration more than 5 mm (p = 0.01). Histopathologic assessment of the primary tumor specimen therefore continues to provide information that is central to guide clinical management, particularly in cases of occult nodal metastases. Clinical significance The study highlights the importance of

Full Text Available BackgroundThe aims of this study are to investigate the glycemic efficacy and predictiveparameters of vildagliptin therapy in Korean subjects with type 2 diabetes.MethodsIn this retrospective study, we retrieved data for subjects who were on twice-daily 50 mg vildagliptin for at least 6 months, and classified the subjects into five treatment groups. In three of the groups, we added vildagliptin to their existing medication regimen; in the other two groups, we replaced one of their existing medications with vildagliptin. We then analyzed the changes in glucose parameters and clinical characteristics.ResultsUltimately, 327 subjects were analyzed in this study. Vildagliptin significantly improved hemoglobin A1c (HbA1c levels over 6 months. The changes in HbA1c levels (ΔHbA1c at month 6 were -2.24% (P=0.000, -0.77% (P=0.000, -0.80% (P=0.001, -0.61% (P=0.000, and -0.34% (P=0.025 for groups 1, 2, 3, 4, and 5, respectively, with significance. We also found significant decrements in fasting plasma glucose levels in groups 1, 2, 3, and 4 (P<0.05. Of the variables, initial HbA1c levels (P=0.032 and history of sulfonylurea use (P=0.026 were independently associated with responsiveness to vildagliptin treatment.ConclusionVildagliptin was effective when it was used in subjects with poor glycemic control. It controlled fasting plasma glucose levels as well as sulfonylurea treatment in Korean type 2 diabetic subjects.

Full Text Available During the machining processes, heat gets generated as a result of plastic deformation of metal and friction along the tool–chip and tool–work piece interface. In materials having high thermal conductivity, like aluminium alloys, large amount of this heat is absorbed by the work piece. This results in the rise in the temperature of the work piece, which may lead to dimensional inaccuracies, surface damage and deformation. So, it is needed to control rise in the temperature of the work piece. This paper focuses on the measurement, analysis and prediction of work piece temperature rise during the dry end milling operation of Al 6063. The control factors used for experimentation were number of flutes, spindle speed, depth of cut and feed rate. The Taguchi method was employed for the planning of experimentation and L18 orthogonal array was selected. The temperature rise of the work piece was measured with the help of K-type thermocouple embedded in the work piece. Signal to noise (S/N ratio analysis was carried out using the lower-the-better quality characteristics. Depth of cut was identified as the most significant factor affecting the work piece temperature rise, followed by spindle speed. Analysis of variance (ANOVA was employed to find out the significant parameters affecting the work piece temperature rise. ANOVA results were found to be in line with the S/N ratio analysis. Regression analysis was used for developing empirical equation of temperature rise. The temperature rise of the work piece was calculated using the regression equation and was found to be in good agreement with the measured values. Finally, confirmation tests were carried out to verify the results obtained. From the confirmation test it was found that the Taguchi method is an effective method to determine optimised parameters for minimization of work piece temperature.

The objective of this study was to determine the harvest period of coffee fruits based on the relationship between agrometeorological parameters and sucrose accumulation in the seeds. Over the crop years 2004/2005 and 2006/2007, from 150 days after flowering (DAF) onwards, samples of 50 fruits of cultivars Mundo Novo IAC 376-4, Obatã IAC 1669-20 and Catuaí Vermelho IAC 144 were collected from coffee trees located in Campinas, Brazil. The endosperm of the fruits was freeze-dried, ground and analyzed for sucrose content by high-performance liquid chromatography. A weather station provided data to calculate the accumulated growing degree-day (GDD) units, and the reference (ETo) and actual (ETr) evapotranspiration rates. The results showed that the highest rates of sucrose accumulation occurred at the transition from the cane-green to the cherry phenological stage. Models for the estimation of sucrose content during maturation based on meteorological variables exhibited similar or better performance than the DAF variable, with better results for the variables GDD and ETo. The Mundo Novo cultivar reached the highest sucrose level in the endosperm after 2,790 GDD, while cultivar Catuaí attained its maximum sucrose concentration after the accumulated evapotranspiration rate has reached a value of 870 mm. As for cultivar Obatã, the maximum sucrose concentration was predicted with the same degree of accuracy using any of the parameters investigated. For the Obatã cultivar, the values of the variables calculated for the maximum sucrose concentration to be reached were 249 DAF, 3,090 GDD, 1,020 ETo and 900 ETr.

We present an analytical, seismologically consistent expression for the surface area of the region within which most landslides triggered by an earthquake are located (landslide distribution area). This expression is based on scaling laws relating seismic moment, source depth, and focal mechanism with ground shaking and fault rupture length and assumes a globally constant threshold of acceleration for onset of systematic mass wasting. The seismological assumptions are identical to those recently used to propose a seismologically consistent expression for the total volume and area of landslides triggered by an earthquake. To test the accuracy of the model we gathered geophysical information and estimates of the landslide distribution area for 83 earthquakes. To reduce uncertainties and inconsistencies in the estimation of the landslide distribution area, we propose an objective definition based on the shortest distance from the seismic wave emission line containing 95 % of the total landslide area. Without any empirical calibration the model explains 56 % of the variance in our dataset, and predicts 35 to 49 out of 83 cases within a factor of 2, depending on how we account for uncertainties on the seismic source depth. For most cases with comprehensive landslide inventories we show that our prediction compares well with the smallest region around the fault containing 95 % of the total landslide area. Aspects ignored by the model that could explain the residuals include local variations of the threshold of acceleration and processes modulating the surface ground shaking, such as the distribution of seismic energy release on the fault plane, the dynamic stress drop, and rupture directivity. Nevertheless, its simplicity and first-order accuracy suggest that the model can yield plausible and useful estimates of the landslide distribution area in near-real time, with earthquake parameters issued by standard detection routines.

Full Text Available Aim of investigation. To study the correlation between lipid metabolism during pregnancy and the risk of preeclampsia (PE. Material and methods. We have studied parameters of lipid metabolism in the blood serum of 267 pregnant women with the help of diagnostic kits. Blood sampling was carried out in I and II gestation trimesters. Total cholesterol (TC and triglycerides (TG were determined by colorimetric, enzymatic methods; high density lipoproteins (HDL — with homogeneous method, low density lipoproteins (LDL — with the direct method. Very low density lipoproteins (VLDL concentration was calculated from the Friedwald equation: VLDL = TG / 2.2. Depending on the clinical picture of PE, 43 pregnant women were divided into groups with mild and moderate-to-severe courses of the disease. Results. Among women with PE, we observed significant changes of the lipid profile parameters in the II trimester. These women had elevated TG levels in the blood serum: I group — 1.73 ± 0.14 mM/l, II group — 1.86 ± 0.18 mM/l as compared to the group III (controls — 1.32 ± 0.29 mM/l; decreased HDL indices: I group — 0.79 ± 0.19 mM/l; group II — 0.64 ± 0.04 mM/l in comparison with the control group — 1.17 ± 0.12 mM/l and increased VLDL — 0.78 ± 0.09 mM/l for group I, 0.90 ± 0.06 mM/l in women of group II unlike group III — 0.60 ± 0.16 mM/l. TC and LDL levels among patients with PE did not differ from pregnant women in the control group. Conclusions. It was demonstrated the existence of an imbalance in the synthesis of lipoproteins in women, whose pregnancies were complicated by development of PE that manifested by hypertriglyceridemia with predominance of atherogenic fractions. It was found that the investigation of the parameters of lipid profile in the II trimester of pregnancy allows us to predict the risk of PE and its long-term cardiovascular and metabolic consequences.

Within recent years, there has been growing interest in the prediction of bull fertility through in vitro assessment of semen quality. A model for fertility prediction based on early evaluation of semen quality parameters, to exclude sires with potentially low fertility from breeding programs, would therefore be useful. The aim of the present study was to identify the most suitable parameters that would provide reliable prediction of fertility. Frozen semen from 18 Italian Holstein-Friesian proven bulls was analyzed using computer-assisted semen analysis (CASA) (motility and kinetic parameters) and flow cytometry (FCM) (viability, acrosomal integrity, mitochondrial function, lipid peroxidation, plasma membrane stability and DNA integrity). Bulls were divided into two groups (low and high fertility) based on the estimated relative conception rate (ERCR). Significant differences were found between fertility groups for total motility, active cells, straightness, linearity, viability and percentage of DNA fragmented sperm. Correlations were observed between ERCR and some kinetic parameters, and membrane instability and some DNA integrity indicators. In order to define a model with high relation between semen quality parameters and ERCR, backward stepwise multiple regression analysis was applied. Thus, we obtained a prediction model that explained almost half (R 2=0.47, P<0.05) of the variation in the conception rate and included nine variables: five kinetic parameters measured by CASA (total motility, active cells, beat cross frequency, curvilinear velocity and amplitude of lateral head displacement) and four parameters related to DNA integrity evaluated by FCM (degree of chromatin structure abnormality Alpha-T, extent of chromatin structure abnormality (Alpha-T standard deviation), percentage of DNA fragmented sperm and percentage of sperm with high green fluorescence representative of immature cells). A significant relationship (R 2=0.84, P<0.05) was observed between

Full Text Available The potential of broadly neutralizing antibodies targeting the HIV-1 envelope trimer to prevent HIV-1 transmission has opened new avenues for therapies and vaccines. However, their implementation remains challenging and would profit from a deepened mechanistic understanding of HIV-antibody interactions and the mucosal transmission process. In this study we experimentally determined stoichiometric parameters of the HIV-1 trimer-antibody interaction, confirming that binding of one antibody is sufficient for trimer neutralization. This defines numerical requirements for HIV-1 virion neutralization and thereby enables mathematical modelling of in vitro and in vivo antibody neutralization efficacy. The model we developed accurately predicts antibody efficacy in animal passive immunization studies and provides estimates for protective mucosal antibody concentrations. Furthermore, we derive estimates of the probability for a single virion to start host infection and the risks of male-to-female HIV-1 transmission per sexual intercourse. Our work thereby delivers comprehensive quantitative insights into both the molecular principles governing HIV-antibody interactions and the initial steps of mucosal HIV-1 transmission. These insights, alongside the underlying, adaptable modelling framework presented here, will be valuable for supporting in silico pre-trial planning and post-hoc evaluation of HIV-1 vaccination or antibody treatment trials.

The main goal of this work is to determine the influence of the work hardening model in the numerical prediction of springback. This study will be performed according with the specifications of the first phase of the 'Benchmark 3' of the Numisheet'2005 Conference: the 'Channel Draw'. Several work hardening constitutive models are used in order to allow a better description of the different material mechanical behavior. Two are classical pure isotropic hardening models described by a power law (Swift) or a Voce type saturation equation. Those two models were also combined with a non-linear (Lemaitre and Chaboche) kinematic hardening rule. The final one is the Teodosiu microstructural hardening model. The study is performed for two commonly used steels of the automotive industry: mild (DC06) and dual phase (DP600) steels. The mechanical characterization, as well as the constitutive parameters identification of each work hardening models, was performed by LPMTM, based on an appropriate set of experimental data such as uniaxial tensile tests, monotonic and Bauschinger simple shear tests and orthogonal strain path tests, all at various orientations with respect to the rolling direction. All the simulations were carried out with the CEMUC's home code DD3IMP (contraction of 'Deep Drawing 3-D IMPlicit code')

One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various “outbreaks” of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus) collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin), in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight) were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River. PMID:27115488

Full Text Available Based on the observation that Ramachandran-type potential energy surfaces of single amino acid units in water are in good agreement with statistical structures of the corresponding amino acid residues in proteins, we recently developed a new all-atom force field called SAAP, in which the total energy function for a polypeptide is expressed basically as a sum of single amino acid potentials and electrostatic and Lennard-Jones potentials between the amino acid units. In this study, the SAAP force field (SAAPFF parameters were improved, and classical canonical Monte Carlo (MC simulation was carried out for short peptide models, that is, Met-enkephalin and chignolin, at 300 K in an implicit water model. Diverse structures were reasonably obtained for Met-enkephalin, while three folded structures, one of which corresponds to a native-like structure with three native hydrogen bonds, were obtained for chignolin. The results suggested that the SAAP-MC method is useful for conformational sampling for the short peptides. A protocol of SAAP-MC simulation followed by structural clustering and examination of the obtained structures by ab initio calculation or simply by the number of the hydrogen bonds (or the hardness was demonstrated to be an effective strategy toward structure prediction for short peptide molecules.

Full Text Available One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various "outbreaks" of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin, in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River.

Full Text Available The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations.

field monitoring. Vibration prediction diminishes the importance of trial-and-error procedures such as drill-off tests, which are valid only for short sections. It also solves an existing lapse in Mechanical Specific Energy (MSE) real-time drilling control programs applying the theory of Teale, which states that a drilling system is perfectly efficient when it spends the exact energy to overcome the in situ rock strength. Using the proprietary software tool this paper will examine the resonant vibration modes that may be initiated while drilling with different BHA's and drill string designs, showing that the combination of a proper BHA design along with the correct selection of input parameters results in an overall improvement to drilling efficiency. Also, being the BHA predictively analyzed, it will be reduced the potential for vibration or stress fatigue in the drill string components, leading to a safer operation. In the recent years there has been an increased focus on vibration detection, analysis, and mitigation techniques, where new technologies, like the Drilling Dynamics Data Recorders (DDDR), may provide the capability to capture high frequency dynamics data at multiple points along the drilling system. These tools allow the achievement of drilling performance improvements not possible before, opening a whole new array of opportunities for optimization and for verification of predictions calculated by the drill string dynamics modeling software tool. The results of this study will identify how the dynamics from the drilling system, interacting with formation, directly relate to inefficiencies and to the possible solutions to mitigate drilling vibrations in order to improve drilling performance. Software vibration prediction and downhole measurements can be used for non-drilling operations like drilling out casing or reaming, where extremely high vibration levels - devastating to the cutting structure of the bit before it has even touched bottom - have

Full Text Available Background: Intrauterine insemination (IUI is one the most common methods in infertility treatment, but its efficiency in infertile couples with male factor is controversial. This study is a retrospective study about correlation between semen parameters and male and female age with successful rate of IUI in patients attending to Royan Institute.Methods: A total of 998 consecutive couples in a period of 6 months undergoing IUI were included. They were classified into two groups: couples with successful and unsuccessful pregnancy. Main outcome was clinical pregnancy. Data about male and female ages and semen analysis including concentration, total sperm motility, class A motility, class B motility, class A+B motility and normal morphology was extracted from patients’ records. Semen samples were collected by masturbation or coitus after 2 to 7 days of abstinence. Their female partners were reported to have no chronic medi-cal conditions and have normal menstrual cycles.Results: One hundred and fifty seven of total 998 cycles (15.7% achieved pregnancy. The average of female age in successful and unsuccessful group was 28.95±4.19 and 30.00±4.56 years, respectively. Mean of male age was 33.97±4.85 years in successful group and 34.44±4.62 years in unsuccessful group. In successful and unsuccessful groups, average of sperm concentration was 53.62±38.45 and 46.26±26.59 (million sperm/ml, normal morphology of sperm was 8.98±4.31 (% and 8.68±4.81 (%, sperm total motility was 47.24±18.92 (% and 43.70±20.22 (% and total motile sperm count was 80.10±63.61 million and 78.57±68.22 million, respectively.Conclusion: There was no significant difference in mean of females’ age and males’ age between successful and unsuccessful groups (P<0.05. In addition, there was no significant difference in semen parameters including concentration, total sperm motility, class A motility, class B motility, class A+B motility and normal morphology between two

Debris flow is a common hazard in the Wenchuan earthquake area. Collapse and Landslide Regions (CLR), caused by earthquakes, could be located from Remote Sensing images. CLR are the direct material source regions for debris flow. The Spatial Distribution of Collapse and Landslide Regions (SDCLR) strongly impact debris-flow formation. In order to depict SDCLR, we referred to Strahler's Hypsometric analysis method and developed 3 functional models to depict SDCLR quantitatively. These models mainly depict SDCLR relative to altitude, basin mouth and main gullies of debris flow. We used the integral of functions as the spatial parameters of SDCLR and these parameters were employed during the process of debris-flows scale predictions. Grouping-occurring debris-flows triggered by the rainstorm, which occurred on September 24th 2008 in Beichuan County, Sichuan province China, were selected to build the empirical equations for debris-flows scale predictions. Given the existing data, only debris-flows runout zone parameters (Max. runout distance L and Lateral width B) were estimated in this paper. The results indicate that the predicted results were more accurate when the spatial parameters were used. Accordingly, we suggest spatial parameters of SDCLR should be considered in the process of debris-flows scale prediction and proposed several strategies to prevent debris flow in the future

Full Text Available This paper presents a study on the effect of unit-cell geometrical parameters in predicting elastic properties of a typical wood plastic composite (WPC. The ultimate goal was obtaining the optimal values of representative volume element (RVE parameters to accurately predict the mechanical behavior of the WPC. For each unit cell, defined by a given combination of the above geometrical parameters, finite element simulation in ABAQUS was carried out, and the corresponding stress-strain curve was obtained. A uniaxial test according to ASTM D638-02a type V was performed on the composite specimen. Modulus of elasticity was determined using hyperbolic tangent function, and the results were compared to the sets of finite element analyses. Main effects of RVE parameters and their interactions were demonstrated and discussed, specially regarding the inclusion of two adjacent wood particles within one unit cell of the material. Regression analysis was performed to mathematically model the RVE parameter effects and their interactions over the modulus of elasticity response. The model was finally employed in an optimization analysis to arrive at an optimal set of RVE parameters that minimizes the difference between the predicted and experimental moduli of elasticity.

In the article, the author develops an analysis of external and intrapsychic factors related to adults' insomnia. First she undertakes a literature review to describe semiological, evolutive and etiological levels of insomnia. From a semiological point of view, it is usual to differenciate initial insomnia (associated to the first phase of sleeping), intermittent insomnia (related to frequent awakenings) and final insomnia (related to early morning awakenings). From an evolutive point of view, we can identify transitory insomnia (characterized by frequent awakenings) and chronic insomnia. On the other hand, we are allowed to distinguish organic insomnia (disorder where an organic cerebral injury is demonstrated or suspected) from insomnias related to psychiatric or somatic disease or idiopathic one. Then, the author makes a literary review to identify various insomnia causes and points out. Social factors: insomnia rates are higher by divorced, separated or widowed people. Percentages are higher when scholastic level is weak, domestic income is less then 915 O a month, or by unemployed people. Besides, sleep quality is deteriorated by ageing. Sleeping and waking rhythm is able to loose its synchronization. Complaints about insomnia occur far frequently from women than men. Environmental factors: working constraints increase sleep disorders. It is possible to make the same conclusion when we have to face overcharge of external events, deep intrapsychic conflicts (related to grief, unemployment, damage or hospitalization) or interpersonal conflicts' situations where we are confronted to stress related to socio-affective environment, lack of social support or conjugal difficulties. Medical and physiologic causes: legs impatience syndrome, recurrent limbs shakings syndrome, breathe stop during sleep, narcolepsy, excessive medicine or hypnotic drugs use, some central nervous system injuries, every nocturnal awakening (related to aches.), surgical operation

The inter-comparisons demonstrated that the Chaos-MLR and pure MLR models yield almost the same accuracy in predicting the significant wave heights and the zero-up-crossing wave periods. Whereas, the augmented Chaos-MLR model is performed better results in term of the prediction accuracy vis-a-vis the previous prediction applications of the same case study.

The objectives of this study were to investigate the predictive value of tumour measurements on 2-deoxy-2-[ 18 F]fluoro-D-glucose ( 18 F-FDG) positron emission tomography (PET) pretreatment scan regarding therapy response in oesophageal cancer and to evaluate the impact of tumour delineation strategies. Fifty patients with oesophageal cancer treated with concomitant radiochemotherapy between 2004 and 2008 were retrospectively considered and classified as complete, partial or non-responders (including stable and progressive disease) according to Response Evaluation Criteria in Solid Tumors (RECIST). The classification of partial and complete responders was confirmed by biopsy. Tumours were delineated on the 18 F-FDG pretreatment scan using an adaptive threshold and the automatic fuzzy locally adaptive Bayesian (FLAB) methodologies. Several parameters were then extracted: maximum and peak standardized uptake value (SUV), tumour longitudinal length (TL) and volume (TV), SUV mean , and total lesion glycolysis (TLG = TV x SUV mean ). The correlation between each parameter and response was investigated using Kruskal-Wallis tests, and receiver-operating characteristic methodology was used to assess performance of the parameters to differentiate patients. Whereas commonly used parameters such as SUV measurements were not significant predictive factors of the response, parameters related to tumour functional spatial extent (TL, TV, TLG) allowed significant differentiation of all three groups of patients, independently of the delineation strategy, and could identify complete and non-responders with sensitivity above 75% and specificity above 85%. A systematic although not statistically significant trend was observed regarding the hierarchy of the delineation methodologies and the parameters considered, with slightly higher predictive value obtained with FLAB over adaptive thresholding, and TLG over TV and TL. TLG is a promising predictive factor of concomitant

The objectives of this study were to investigate the predictive value of tumour measurements on 2-deoxy-2-[{sup 18}F]fluoro-D-glucose ({sup 18}F-FDG) positron emission tomography (PET) pretreatment scan regarding therapy response in oesophageal cancer and to evaluate the impact of tumour delineation strategies. Fifty patients with oesophageal cancer treated with concomitant radiochemotherapy between 2004 and 2008 were retrospectively considered and classified as complete, partial or non-responders (including stable and progressive disease) according to Response Evaluation Criteria in Solid Tumors (RECIST). The classification of partial and complete responders was confirmed by biopsy. Tumours were delineated on the {sup 18}F-FDG pretreatment scan using an adaptive threshold and the automatic fuzzy locally adaptive Bayesian (FLAB) methodologies. Several parameters were then extracted: maximum and peak standardized uptake value (SUV), tumour longitudinal length (TL) and volume (TV), SUV{sub mean}, and total lesion glycolysis (TLG = TV x SUV{sub mean}). The correlation between each parameter and response was investigated using Kruskal-Wallis tests, and receiver-operating characteristic methodology was used to assess performance of the parameters to differentiate patients. Whereas commonly used parameters such as SUV measurements were not significant predictive factors of the response, parameters related to tumour functional spatial extent (TL, TV, TLG) allowed significant differentiation of all three groups of patients, independently of the delineation strategy, and could identify complete and non-responders with sensitivity above 75% and specificity above 85%. A systematic although not statistically significant trend was observed regarding the hierarchy of the delineation methodologies and the parameters considered, with slightly higher predictive value obtained with FLAB over adaptive thresholding, and TLG over TV and TL. TLG is a promising predictive factor of

Importance of the field Due to growing concerns over toxic or active metabolites, significant efforts have been focused on qualitative identification of potential in vivo metabolites from in vitro data. However, limited tools are available to quantitatively predict their human exposures. Areas covered in this review Theory of clearance predictions and metabolite kinetics is reviewed together with supporting experimental data. In vitro and in vivo data of known circulating metabolites and their parent drugs was collected and the predictions of in vivo exposures of the metabolites were evaluated. What the reader will gain The theory and data reviewed will be useful in early identification of human metabolites that will circulate at significant levels in vivo and help in designing in vivo studies that focus on characterization of metabolites. It will also assist in rationalization of metabolite-to-parent ratios used as markers of specific enzyme activity. Take home message The relative importance of a metabolite in comparison to the parent compound as well as other metabolites in vivo can only be predicted using the metabolites in vitro formation and elimination clearances, and the in vivo disposition of a metabolite can only be rationalized when the elimination pathways of that metabolite are known. PMID:20557268

of patients with hyperhidrosis has been reported. Conclusions: Despite these accumulated data, the etiology of primary hyperhidrosis remains obscure. Nevertheless, three main lines for future research seem to be delineated: genetics, histological observations, and enzymatic studies.......Purpose: Primary hyperhidrosis is a pathological disorder of unknown etiology, affecting 0.6-5% of the population, and causing severe functional and social handicaps. As the etiology is unknown, it is not possible to treat the root cause. Recently some differences between affected and non......-affected people have been reported. The aim of this review is to summarize these new etiological data. Methods: Search of the literature was performed in the PubMed/Medline Database and pertinent articles were retrieved and reviewed. Additional publications were obtained from the references of these articles...

Full Text Available In this study, a comparative approach was made between artificial neural network (ANN and response surface methodology (RSM to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE, mean absolute error (MAE, standard error of prediction (SEP, model predictive error (MPE, chi square statistic (χ2, and coefficient of determination (R2 based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.

Estimation of parameter and predictive uncertainty of hydrologic models has traditionally relied on several simplifying assumptions. Residual errors are often assumed to be independent and to be adequately described by a Gaussian probability distribution with a mean of zero and a constant variance.

The Grüneisen parameter (γ) is very important to decide the limitations for the prediction of thermoelastic properties of bulk metallic glasses. It can be defined in terms of microscopic and macroscopic parameters of the material in which former is based on vibrational frequencies of atoms in the material while later is closely related to its thermodynamic properties. Different formulation and equation of states are used by the pioneer researchers of this field to predict the true sense of Gruneisen parameter for BMG but for SiO_2.TiO_2 very few and insufficient information is available till now. In the present work we have tested the validity of two different isothermal EOS viz. Poirrior-Tarantola EOS and Usual-Tait EOS to predict the true value of Gruneisen parameter for SiO_2.TiO_2 as a function of compression. Using different thermodynamic limitations related to the material constraints and analyzing obtained result it is concluded that the Poirrior-Tarantola EOS gives better numeric values of Grüneisen parameter (γ) for SiO_2.TiO_2 BMG.

INTRODUCTION: Iron parameters like serum ferritin and iron saturation are routinely used in diagnosing iron deficiency. However, these tests are influenced by many factors. We aimed to review the accuracy of iron parameters among inpatients in an acute care hospital. MATERIALS AND METHODS: From

Generally, parameters that are nonlinear in system models are estimated by nonlinear least-squares optimization algorithms. In this paper, if a nonlinear discrete-time model with a polynomial quotient structure in input, output, and parameters, a method is proposed to re-parameterize the model such

Analysis centers (ACs) for global navigation satellite systems (GNSSs) cannot accurately obtain real-time Earth rotation parameters (ERPs). Thus, the prediction of ultra-rapid orbits in the international terrestrial reference system (ITRS) has to utilize the predicted ERPs issued by the International Earth Rotation and Reference Systems Service (IERS) or the International GNSS Service (IGS). In this study, the accuracy of ERPs predicted by IERS and IGS is analyzed. The error of the ERPs predicted for one day can reach 0.15 mas and 0.053 ms in polar motion and UT1-UTC direction, respectively. Then, the impact of ERP errors on ultra-rapid orbit prediction by GNSS is studied. The methods for orbit integration and frame transformation in orbit prediction with introduced ERP errors dominate the accuracy of the predicted orbit. Experimental results show that the transformation from the geocentric celestial references system (GCRS) to ITRS exerts the strongest effect on the accuracy of the predicted ultra-rapid orbit. To obtain the most accurate predicted ultra-rapid orbit, a corresponding real-time orbit correction method is developed. First, orbits without ERP-related errors are predicted on the basis of ITRS observed part of ultra-rapid orbit for use as reference. Then, the corresponding predicted orbit is transformed from GCRS to ITRS to adjust for the predicted ERPs. Finally, the corrected ERPs with error slopes are re-introduced to correct the predicted orbit in ITRS. To validate the proposed method, three experimental schemes are designed: function extrapolation, simulation experiments, and experiments with predicted ultra-rapid orbits and international GNSS Monitoring and Assessment System (iGMAS) products. Experimental results show that using the proposed correction method with IERS products considerably improved the accuracy of ultra-rapid orbit prediction (except the geosynchronous BeiDou orbits). The accuracy of orbit prediction is enhanced by at least 50

Full Text Available OBJECTIVE: We developed a predictive model for significant fibrosis in chronic hepatitis B (CHB based on routinely available clinical parameters. METHODS: 237 treatment-naïve CHB patients [58.4% hepatitis B e antigen (HBeAg-positive] who had undergone liver biopsy were randomly divided into two cohorts: training group (n = 108 and validation group (n = 129. Liver histology was assessed for fibrosis. All common demographics, viral serology, viral load and liver biochemistry were analyzed. RESULTS: Based on 12 available clinical parameters (age, sex, HBeAg status, HBV DNA, platelet, albumin, bilirubin, ALT, AST, ALP, GGT and AFP, a model to predict significant liver fibrosis (Ishak fibrosis score ≥3 was derived using the five best parameters (age, ALP, AST, AFP and platelet. Using the formula log(index+1 = 0.025+0.0031(age+0.1483 log(ALP+0.004 log(AST+0.0908 log(AFP+1-0.028 log(platelet, the PAPAS (Platelet/Age/Phosphatase/AFP/AST index predicts significant fibrosis with an area under the receiving operating characteristics (AUROC curve of 0.776 [0.797 for patients with ALT <2×upper limit of normal (ULN] The negative predictive value to exclude significant fibrosis was 88.4%. This predictive power is superior to other non-invasive models using common parameters, including the AST/platelet/GGT/AFP (APGA index, AST/platelet ratio index (APRI, and the FIB-4 index (AUROC of 0.757, 0.708 and 0.723 respectively. Using the PAPAS index, 67.5% of liver biopsies for patients being considered for treatment with ALT <2×ULN could be avoided. CONCLUSION: The PAPAS index can predict and exclude significant fibrosis, and may reduce the need for liver biopsy in CHB patients.

Full Text Available Abstract Background A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. Results The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. Conclusion Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.

). A starting point was the authoritative conclusion (Coletta et al., 1978), that permeation in protective clothing could not be predicted. As a spin off, the predictive concept indicated that new types of polymers sometimes should be incorporated to reach a reasonable (long) breakthrough time and (low...

Full Text Available This study developed a GIS-based multivariate regression (MVR yield rate prediction model of groundwater resource sustainability in the hard-rock geology terrain of southwestern Nigeria. This model can economically manage the aquifer yield rate potential predictions that are often overlooked in groundwater resources development. The proposed model relates the borehole yield rate inventory of the area to geoelectrically derived parameters. Three sets of borehole yield rate conditioning geoelectrically derived parameters—aquifer unit resistivity (ρ, aquifer unit thickness (D and coefficient of anisotropy (λ—were determined from the acquired and interpreted geophysical data. The extracted borehole yield rate values and the geoelectrically derived parameter values were regressed to develop the MVR relationship model by applying linear regression and GIS techniques. The sensitivity analysis results of the MVR model evaluated at P ⩽ 0.05 for the predictors ρ, D and λ provided values of 2.68 × 10−05, 2 × 10−02 and 2.09 × 10−06, respectively. The accuracy and predictive power tests conducted on the MVR model using the Theil inequality coefficient measurement approach, coupled with the sensitivity analysis results, confirmed the model yield rate estimation and prediction capability. The MVR borehole yield prediction model estimates were processed in a GIS environment to model an aquifer yield potential prediction map of the area. The information on the prediction map can serve as a scientific basis for predicting aquifer yield potential rates relevant in groundwater resources sustainability management. The developed MVR borehole yield rate prediction mode provides a good alternative to other methods used for this purpose.

Studies of parameter variability by Monte Carlo analysis are reviewed using repeated simulations of the model with randomly selected parameter values. At the beginning of each simulation, parameter values are chosen from specific frequency distributions. This process is continued for a number of iterations sufficient to converge on an estimate of the frequency distribution of the output variables. The purpose was to explore the general properties of error propagaton in models. Testing the implicit assumptions of analytical methods and pointing out counter-intuitive results produced by the Monte Carlo approach are additional points covered

Probabilistic runoff forecasts generated by stochastic greybox models can be notably useful for the improvement of the decision-making process in real-time control setups for urban drainage systems because the prediction risk relationships in these systems are often highly nonlinear. To date...... the identification of models for cases with noisy in-sewer observations. For the prediction of the overflow risk, no improvement was demonstrated through the application of stochastic forecasts instead of point predictions, although this result is thought to be caused by the notably simplified setup used...

... Parameter Identification of Accelerating Aircraft. Flutter clearance, which is part of any new aircraft or fighter weapon system development, is a lengthy and tedious process from both computational and flight testing viewpoint...

Full Text Available Deteriorating systems, which are subject to both continuous smooth degradation and additional abrupt damages due to a shock process, can be often encountered in engineering. Modeling the degradation evolution and predicting the lifetime of this kind of systems are both interesting and challenging in practice. In this paper, we model the degradation trajectory of the deteriorating system by a random coefficient regression (RCR model with positive jumps, where the RCR part is used to model the continuous smooth degradation of the system and the jump part is used to characterize the abrupt damages due to random shocks. Based on a specified threshold level, the probability density function (PDF and cumulative distribution function (CDF of the lifetime can be derived analytically. The unknown parameters associated with the derived lifetime distributions can be estimated via a well-designed parameter estimation procedure on the basis of the available degradation recordings of the deteriorating systems. An illustrative example is finally provided to demonstrate the implementation and superiority of the newly proposed lifetime prediction method. The experimental results reveal that our proposed lifetime prediction method with the dedicated parameter estimation strategy can get more accurate lifetime predictions than the rival model in literature.

In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors. The variable selection method of ERM was employed to select an optimum subset of descriptors. The five descriptors selected using ERM were used as inputs of SVM to predict the polarity parameter of organic compounds in methanol. The coefficient of determination, r 2 , between experimental and predicted polarity parameters for the prediction set by ERM and SVM were 0.952 and 0.982, respectively. Acceptable results specified that the ERM approach is a very effective method for variable selection and the predictive aptitude of the SVM model is superior to those obtained by ERM. The obtained results demonstrate that SVM can be used as a substitute influential modeling tool for QSPR studies.

Highlights: • Adomian decomposition is used to study a moving fin. • Effects of different parameters on the temperature and efficiency are studied. • Binary-coded GA is used to solve an inverse problem. • Sensitivity analyses of important parameters are carried out. • Measurement error up to 8% is found to be tolerable. - Abstract: The application of the Adomian decomposition method (ADM) is extended to study a conductive–convective and radiating moving fin having variable thermal conductivity. Next, through an inverse approach, ADM in conjunction with a binary-coded genetic algorithm (GA) is also applied for estimation of unknown properties in order to satisfy a given temperature distribution. ADM being one of the widely-used numerical methods for solving non-linear equations, the required temperature field has been obtained using a forward method involving ADM. In the forward problem, the temperature field and efficiency are investigated for various parameters such as convection–conduction parameter, radiation–conduction parameter, Peclet number, convection sink temperature, radiation sink temperature, and dimensionless thermal conductivity. Additionally, in the inverse problem, the effect of random measurement errors, iterative variation of parameters, sensitivity coefficients of unknown parameters are investigated. The performance of GA is compared with few other optimization methods as well as with different temperature measurement points. It is found from the present study that the results obtained from ADM are in good agreement with the results of the differential transformation method available in the literature. It is also observed that for satisfactory reconstruction of the temperature field, the measurement error should be within 8% and the temperature field is strongly dependent on the speed than thermal parameters of the moving fin

It is not well established whether pretreatment 18 F-FDG PET/CT can predict local response of head and neck squamous cell carcinoma (HNSCC) to chemoradiotherapy (CRT). We examined 118 patients: 11 with nasopharyngeal cancer (NPC), 30 with oropharyngeal cancer (OPC), and 77 with laryngohypopharyngeal cancer (LHC) who had completed CRT. PET/CT parameters of primary tumor, including metabolic tumor volume (MTV), total lesion glycolysis (TLG), and maximum and mean standardized uptake value (SUV max and SUV mean ), were correlated with local response, according to primary site and human papillomavirus (HPV) status. Receiver-operating characteristic analyses were made to access predictive values of the PET/CT parameters, while logistic regression analyses were used to identify independent predictors. Area under the curve (AUC) of the PET/CT parameters ranged from 0.53 to 0.63 in NPC and from 0.50 to 0.54 in OPC. HPV-negative OPC showed AUC ranging from 0.51 to 0.58, while all of HPV-positive OPCs showed complete response. In contrast, AUC ranged from 0.71 to 0.90 in LHC. Moreover, AUCs of MTV and TLG were significantly higher than those of SUV max and SUV mean (P < 0.01). After multivariate analysis, high MTV >25.0 mL and high TLG >144.8 g remained as independent, significant predictors of incomplete response compared with low MTV (odds ratio [OR], 13.4; 95% confidence interval [CI], 2.5–72.9; P = 0.003) and low TLG (OR, 12.8; 95% CI, 2.4–67.9; P = 0.003), respectively. In conclusion, predictive efficacy of pretreatment 18 F-FDG PET/CT varies with different primary sites and chosen parameters. Local response of LHC is highly predictable by volume-based PET/CT parameters

We analyzed postsurgery linguistic outcomes of 43 hemispherectomy patients operated on at UCLA. We rated spoken language (Spoken Language Rank, SLR) on a scale from 0 (no language) to 6 (mature grammar) and examined the effects of side of resection/damage, age at surgery/seizure onset, seizure control postsurgery, and etiology on language development. Etiology was defined as developmental (cortical dysplasia and prenatal stroke) and acquired pathology (Rasmussen's encephalitis and postnatal stroke). We found that clinical variables were predictive of language outcomes only when they were considered within distinct etiology groups. Specifically, children with developmental etiologies had lower SLRs than those with acquired pathologies (p =.0006); age factors correlated positively with higher SLRs only for children with acquired etiologies (p =.0006); right-sided resections led to higher SLRs only for the acquired group (p =.0008); and postsurgery seizure control correlated positively with SLR only for those with developmental etiologies (p =.0047). We argue that the variables considered are not independent predictors of spoken language outcome posthemispherectomy but should be viewed instead as characteristics of etiology. Copyright 2001 Elsevier Science.

The reliability and lifetime of machine elements such as gears and rolling bearings depend on their wear and fatigue resistance. In order to screen the wear and surface damage, three finishing processes: (i) brushing, (ii) manganese phosphating and (iii) shot peening were applied on three disc pairs and long-term tested on a twin-disc tribometer. In this paper, the elastic contact of the disc surfaces (measured after only few revolutions) was simulated and a number of functional and roughness parameters were correlated. The functional parameters consisted of subsurface stresses at different depths and a new parameter called ‘pressure spikes’ factor’. The new parameter is derived from the pressure distribution and takes into account the proximity and magnitude of the pressure spikes. Strong correlations were found among the pressure spikes’ factor and surface peak/height parameters. The orthogonal shear stresses and Von Mises stresses at the shallowest depths under the surface have shown the highest correlations but no good correlations were found when the statistics of the whole stress fields was analyzed. The use of the new parameter offers a fast way to screen the durability of the contacting surfaces operating at similar conditions.

Agility is a fundamental performance element in many sports, but poses a high risk of injury. Hierarchical modelling has shown that eccentric hamstring strength is the primary determinant of agility performance. The purpose of this study was to investigate the relationship between knee flexor and extensor strength parameters and a battery of agility tests. Controlled laboratory study. Nineteen recreational intermittent games players completed an agility battery and isokinetic testing of the eccentric knee flexors (eccH) and concentric knee extensors (conQ) at 60, 180 and 300°·s -1 . Peak torque and the angle at which peak torque occurred were calculated for eccH and conQ at each speed. Dynamic control ratios (eccH:conQ) and fast:slow ratios (300:60) were calculated using peak torque values, and again using angle-matched data, for eccH and conQ. The agility test battery differentiated linear vs directional changes and prescriptive vs reactive tasks. Linear regression showed that eccH parameters were generally a better predictor of agility performance than conQ parameters. Stepwise regression showed that only angle-matched strength ratios contributed to the prediction of each agility test. Trdaitionally calculated strength ratios using peak torque values failed to predict performance. Angle-matched strength parameters were able to account for 80% of the variation in T-test performance, 70% of deceleration distance, 55% of 10m sprint performance, and 44% of reactive change of direction speed. Traditionally calculated strength ratios failed to predict agility performance, whereas angle-matched strength ratios had better predictive ability and featured in a predictive stepwise model for each agility task. 2c.

Full Text Available OBJECTIVE: Perfectionism has been recognized as a transdiagnostic factor that is relevant to anxiety disorders, eating disorders and depression. Despite the importance of perfectionism in psychopathology to date there has been no empirical test of an etiological model of perfectionism. METHOD: The present study aimed to address the paucity of research on the etiology of perfectionism by developing and testing an etiological model using a sample of 311 clients seeking treatment. RESULTS: Structural equation modeling showed a direct relationship between high Parental Expectations and Criticism, and Perfectionism. There was also an indirect relationship between Parental Bonding and Perfectionism that was mediated by core schemas of disconnection and rejection. Finally, it was found that Neuroticism had both an indirect relationship, which was mediated by core schemas, and a direct relationship with perfectionism. CONCLUSIONS: The study provided the first direct test of an etiological model of perfectionism to date. Clinical implications include investigating whether the inclusion of etiological factors in the understanding and treatment of perfectionism is effective.

Present work deals with prediction of surface roughness using cutting parameters along with in-process measured cutting force and tool vibration (acceleration) during turning of Ti-6Al-4V with cubic boron nitride (CBN) inserts. Full factorial design is used for design of experiments using cutting speed, feed rate and depth of cut as design variables. Prediction model for surface roughness is developed using response surface methodology with cutting speed, feed rate, depth of cut, resultant cutting force and acceleration as control variables. Analysis of variance (ANOVA) is performed to find out significant terms in the model. Insignificant terms are removed after performing statistical test using backward elimination approach. Effect of each control variables on surface roughness is also studied. Correlation coefficient (R2 pred) of 99.4% shows that model correctly explains the experiment results and it behaves well even when adjustment is made in factors or new factors are added or eliminated. Validation of model is done with five fresh experiments and measured forces and acceleration values. Average absolute error between RSM model and experimental measured surface roughness is found to be 10.2%. Additionally, an artificial neural network model is also developed for prediction of surface roughness. The prediction results of modified regression model are compared with ANN. It is found that RSM model and ANN (average absolute error 7.5%) are predicting roughness with more than 90% accuracy. From the results obtained it is found that including cutting force and vibration for prediction of surface roughness gives better prediction than considering only cutting parameters. Also, ANN gives better prediction over RSM models.

National Aeronautics and Space Administration — We will combine part-level FEM model of residual stresses with phase-field transformation model to predict deformation and cracking due to thermal stresses from the...

In a reactor accident like loss of coolant accident , one or more signals may not be monitored by control panel for some reasons such as interruptions and so on. Therefore a fast alternative method could guarantee the safe and reliable exploration of nuclear power planets. In this study, we used artificial neural network with Elman recurrent structure to predict six thermal hydraulic signals in a loss of coolant accident after upper plenum break. In the prediction procedure, a few previous samples are fed to the artificial neural network and the output value or next time step is estimated by the network output. The Elman recurrent network is trained with the data obtained from the benchmark simulation of loss of coolant accident in VVER. The results reveal that the predicted values follow the real trends well and artificial neural network can be used as a fast alternative prediction tool in loss of coolant accident

This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is

Full Text Available OBJECTIVE: Evaluation of diabetic sensorimotor polyneuropathy (DSP is hindered by the need for complex nerve conduction study (NCS protocols and lack of predictive biomarkers. We aimed to determine the performance of single and simple combinations of NCS parameters for identification and future prediction of DSP. MATERIALS AND METHODS: 406 participants (61 with type 1 diabetes and 345 with type 2 diabetes with a broad spectrum of neuropathy, from none to severe, underwent NCS to determine presence or absence of DSP for cross-sectional (concurrent validity analysis. The 109 participants without baseline DSP were re-evaluated for its future onset (predictive validity. Performance of NCS parameters was compared by area under the receiver operating characteristic curve (AROC. RESULTS: At baseline there were 246 (60% Prevalent Cases. After 3.9 years mean follow-up, 25 (23% of the 109 Prevalent Controls that were followed became Incident DSP Cases. Threshold values for peroneal conduction velocity and sural amplitude potential best identified Prevalent Cases (AROC 0.90 and 0.83, sensitivity 80 and 83%, specificity 89 and 72%, respectively. Baseline tibial F-wave latency, peroneal conduction velocity and the sum of three lower limb nerve conduction velocities (sural, peroneal, and tibial best predicted 4-year incidence (AROC 0.79, 0.79, and 0.85; sensitivity 79, 70, and 81%; specificity 63, 74 and 77%, respectively. DISCUSSION: Individual NCS parameters or their simple combinations are valid measures for identification and future prediction of DSP. Further research into the predictive roles of tibial F-wave latencies, peroneal conduction velocity, and sum of conduction velocities as markers of incipient nerve injury is needed to risk-stratify individuals for clinical and research protocols.

Full Text Available To capture the spatial and temporal variability of the gross primary production as a key component of the global carbon cycle, the light use efficiency modeling approach in combination with remote sensing data has shown to be well suited. Typically, the model parameters, such as the maximum light use efficiency, are either set to a universal constant or to land class dependent values stored in look-up tables. In this study, we employ the machine learning technique support vector regression to explicitly relate the model parameters of a light use efficiency model calibrated at several FLUXNET sites to site-specific characteristics obtained by meteorological measurements, ecological estimations and remote sensing data. A feature selection algorithm extracts the relevant site characteristics in a cross-validation, and leads to an individual set of characteristic attributes for each parameter. With this set of attributes, the model parameters can be estimated at sites where a parameter calibration is not possible due to the absence of eddy covariance flux measurement data. This will finally allow a spatially continuous model application. The performance of the spatial extrapolation scheme is evaluated with a cross-validation approach, which shows the methodology to be well suited to recapture the variability of gross primary production across the study sites.

This handbook provides generic parameter values for estimating the transfer of radionuclides from environmental media to wildlife for the purpose of assessing potential radiation exposure under equilibrium conditions. These data are intended for use where site specific data are either not available or not required, and to parameterize generic assessment models. They are based on a comprehensive review of the available literature, including many Russian language publications that have not previously been available in English. The publication addresses the limitations of the parameter values and the applicability of data. Some general background information on the assessment of potential impacts of radioactive releases on wildlife is also included. It complements the existing handbook in the same IAEA series with parameter to assess the radiological impact to humans.

Full Text Available For the frequency response analysis of acoustic field with random and interval parameters, a nonintrusive uncertain analysis method named Polynomial Chaos Response Surface (PCRS method is proposed. In the proposed method, the polynomial chaos expansion method is employed to deal with the random parameters, and the response surface method is used to handle the interval parameters. The PCRS method does not require efforts to modify model equations due to its nonintrusive characteristic. By means of the PCRS combined with the existing interval analysis method, the lower and upper bounds of expectation, variance, and probability density function of the frequency response can be efficiently evaluated. Two numerical examples are conducted to validate the accuracy and efficiency of the approach. The results show that the PCRS method is more efficient compared to the direct Monte Carlo simulation (MCS method based on the original numerical model without causing significant loss of accuracy.

This Handbook has been prepared in response to a widely expressed interest in having a convenient and authoritative reference for radionuclide transfer parameter values used in biospheric assessment models. It draws on data from North America and Europe, much of which was collected through projects of the International Union of Radioecologists (IUR) and the Commission of European Communities (CEC) over the last decade. It is intended to supplement existing IAEA publications on environmental assessment methodology, primarily Generic Models and Parameters for Assessing the Environmental Transfer of Radionuclides from Routine Releases, IAEA Safety Series No. 57 (1982). 219 refs, 3 figs, 32 tabs

The static and fatigue tests have been carried out to verify the validity of a generalized residual strength degradation model. And a new method of parameter determination in the model is verified experimentally to account for the effect of tension-compression fatigue loading of spheroidal graphite cast iron. It is shown that the correlation between the experimental results and the theoretical prediction on the statistical distribution of fatigue life by using the proposed method is very reasonable. Furthermore, it is found that the correlation between the theoretical prediction and the experimental results of fatigue life in case of tension-tension fatigue data in composite material appears to be reasonable. Therefore, the proposed method is more adjustable in the determination of the parameter than maximum likelihood method and minimization technique

A model is proposed that uses a numerical characterization of the crack tip stress field modified by the J - Q constraint theory and a weak link assumption to predict fracture behavior in the transition for reactor vessel steels. This model predicts the toughness scatter band for a component model from a toughness scatter band measured on a test specimen geometry. The model has been applied previously to two-dimensional through cracks. Many applications to actual components structures involve three-dimensional surface flaws. These cases require a more difficult level of analysis and need additional information. In this paper, both the current model for two-dimensional cracks and an approach needed to extend the model for the prediction of transition fracture behavior in three-dimensional surface flaws are discussed. Examples are presented to show how the model can be applied and in some cases to compare with other test results. (author). 13 refs., 7 figs

Highlights: ► Thermal–hydraulics parameters of a VVER-1000 core based on neural network (ANN), are carried out. ► Required data for ANN training are found based on modified COBRA-EN code and then linked each other using MATLAB software. ► Based on ANN method, average and maximum temperature of fuel and clad as well as MDNBR of each FA are predicted. -- Abstract: The main goal of the present article is to design a computational tool to predict physical parameters of the VVER-1000 nuclear reactor core based on artificial neural network (ANN), taking into account a detailed physical model of the fuel rods and coolant channels in a fuel assembly. Predictions of thermal characteristics of fuel, clad and coolant are performed using cascade feed forward ANN based on linear fission power distribution and power peaking factors of FAs and hot channels factors (which are found based on our previous neutronic calculations). A software package has been developed to prepare the required data for ANN training which applies a modified COBRA-EN code for sub-channel analysis and links the codes using the MATLAB software. Based on the current estimation system, five main core TH parameters are predicted, which include the average and maximum temperatures of fuel and clad as well as the minimum departure from nucleate boiling ratio (MDNBR) for each FA. To get the best conditions for the considered ANNs training, a comprehensive sensitivity study has been performed to examine the effects of variation of hidden neurons, hidden layers, transfer functions, and the learning algorithms on the training and simulation results. Performance evaluation results show that the developed ANN can be trained to estimate the core TH parameters of a typical VVER-1000 reactor quickly without loss of accuracy.

The goal of this study is to realize real-time predictions of the peak power/state of power (SOP) for lithium-ion batteries in electric vehicles (EVs). To allow the proposed method to be applicable to different temperature and aging conditions, a training-free battery parameter/state estimator is presented based on an equivalent circuit model using a dual extended Kalman filter (DEKF). In this estimator, the model parameters are no longer taken as functions of factors such as SOC (state of charge), temperature, and aging; instead, all parameters will be directly estimated under the present conditions, and the impact of the temperature and aging on the battery model will be included in the parameter identification results. Then, the peak power/SOP will be calculated using the estimated results under the given limits. As an improvement to the calculation method, a combined limit of current and voltage is proposed to obtain results that are more reasonable. Additionally, novel verification experiments are designed to provide the true values of the cells' peak power under various operating conditions. The proposed methods are implemented in experiments with LiFePO 4 /graphite cells. The validating results demonstrate that the proposed methods have good accuracy and high adaptability. - Highlights: • A real-time peak power/SOP prediction method for lithium-ion batteries is proposed. • A training-free method based on DEKF is presented for parameter identification. • The proposed method can be applied to different temperature and aging conditions. • The calculation of peak power under the current and voltage limits is improved. • Validation experiments are designed to verify the accuracy of prediction results

In this paper we investigate thermal network models with different model orders applied to various Dutch low-energy house types with high and low interior thermal mass and containing floor heating. Parameter estimations are performed by using data from TRNSYS simulations. The paper discusses results

in three scenarios involving simulation of groundwater head distribution and travel time. The first scenario implied 100 stochastic geological models all assigning the same hydraulic parameters for the same geological units. In the second scenario the same 100 geological models were subjected to model...

Full Text Available Abstract Currently, the assessment of sperm function in a raw or processed semen sample is not able to reliably predict sperm ability to withstand freezing and thawing procedures and in vivo fertility and/or assisted reproductive biotechnologies (ART outcome. The aim of the present study was to investigate which parameters among a battery of analyses could predict subsequent spermatozoa in vitro fertilization ability and hence blastocyst output in a goat model. Ejaculates were obtained by artificial vagina from 3 adult goats (Capra hircus aged 2 years (A, B and C. In order to assess the predictive value of viability, computer assisted sperm analyzer (CASA motility parameters and ATP intracellular concentration before and after thawing and of DNA integrity after thawing on subsequent embryo output after an in vitro fertility test, a logistic regression analysis was used. Individual differences in semen parameters were evident for semen viability after thawing and DNA integrity. Results of IVF test showed that spermatozoa collected from A and B lead to higher cleavage rates (0

The methodology for the prediction of properties of environmental relevance of polycyclic aromatic hydrocarbons based on the conductor-like screening model for real solvents (COSMO-RS/COSMOtherm) is presented and evaluated, with a special focus on the aqueous solubility of polycyclic aromatic

Prediction of flow through variably saturated porous media requires accurate knowledge of the soil hydraulic properties, namely the water retention function (WRF) and the hydraulic conductivity function (HCF). Unfortunately, direct measurement of the HCF is time consuming and expensive. In this

Occupants’ behaviour related to building control system plays a significant role to achieve thermal comfort and air quality in naturally-ventilated buildings. Generally, the published models of occupant's behavior are not validated, meaning that the predictive power has not yet been tested. For t...

Full Text Available Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0, the diffusion coefficient (D, and the partition coefficient (K, can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.

Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.

Full Text Available The purpose of this study is to evaluate the predictive performance of magnetic resonance imaging (MRI markers in breast cancer patients by subtype. Sixty-four patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy were enrolled in this study. Each patient received a dynamic contrast-enhanced (DCE-MRI at baseline, after 1 cycle of chemotherapy and before surgery. Functional tumor volume (FTV, the imaging marker measured by DCE-MRI, was computed at various thresholds of percent enhancement (PEt and signal-enhancement ratio (SERt. Final FTV before surgery and percent changes of FTVs at the early and final treatment time points were used to predict patients' recurrence-free survival. The full cohort and each subtype defined by the status of hormone receptor and human epidermal growth factor receptor 2 (HR+/HER2-, HER2+, triple negative were analyzed. Predictions were evaluated using the Cox proportional hazard model when PEt changed from 30% to 200% in steps of 10% and SERt changed from 0 to 2 in steps of 0.2. Predictions with high hazard ratios and low p-values were considered as strong. Different profiles of FTV as predictors for recurrence-free survival were observed in each breast cancer subtype and strong associations with survival were observed at different PEt/SERt combinations that resulted in different FTVs. Findings from this retrospective study suggest that the predictive performance of imaging markers based on FTV may be improved with enhancement thresholds being optimized separately for clinically-relevant subtypes defined by HR and HER2 receptor expression.

The worldwide use of kerosene as aviation jet fuel makes its safety considerations of most importance not only for aircraft security but for the workers' health (chronic and/or acute exposure). As most kerosene risks come from its vapours, this work focuses on predicting seven characteristics (flash point, freezing point, % of aromatics and four distillation points) which assess its potential hazards. Two experimental devices were implemented in order to, first, generate a kerosene vapour phase and, then, to measure its mid-IR spectrum. All the working conditions required to generate the gas phase were optimised either in a univariate or a multivariate (SIMPLEX) approach. Next, multivariate prediction models were deployed using partial least squares regression and it was found that both the average prediction errors and precision parameters were satisfactory, almost always well below the reference figures.

Different kinds of neural networks have established themselves as an effective tool in the prediction of different geomagnetic indices, including the Dst being the most important constituent for determination of the impact of Space Weather on the human life. Feed-forward networks with one hidden layer are used to forecast the Dst variation, using separately the solar wind paramenters, polar cap index, and auroral electrojet index as input parameters. It was found that in all three cases the storm-time intervals were predicted much more precisely as quite time intervals. The majority of cross-correlation coefficients between predicted and observed Dst of strong geomagnetic storms are situated between 0.8 and 0.9. Changes in the neural network architecture, including the number of nodes in the input and hidden layers and the transfer functions between them lead to an improvement of a network performance up to 10%.

The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast radiotherapy where the observed incidence is fairly low. From our previous study based on 150 breast cancer patients, where the fits of dose-volume models to clinical data were estimated (Tsougos et al 2005 Evaluation of dose-response models and parameterspredicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy Phys. Med. Biol. 50 3535-54), one could get the impression that the relative seriality is significantly better than the LKB NTCP model. However, the estimation of the different NTCP models was based on their goodness-of-fit on clinical data, using various sets of published parameters from other groups, and this fact may provisionally justify the results. Hence, we sought to investigate further the LKB model, by applying different published parameter sets for the very same group of patients, in order to be able to compare the results. It was shown that, depending on the parameter set applied, the LKB model is able to predict the incidence of radiation pneumonitis with acceptable accuracy, especially when implemented on a sub-group of patients (120) receiving D-bar-bar vertical bar EUD higher than 8 Gy. In conclusion, the goodness-of-fit of a certain radiobiological model on a given clinical case is closely related to the selection of the proper scoring criteria and parameter set as well as to the compatibility of the clinical case from which the data were derived. (letter to the editor)

Four cases of double monsters are reported, including a rare case of craniofacial duplication (diprosopus). Based on the findings observed, etiological factors of these malformations are discussed. We suggest that exogenous (environmental) factors such as habits, way of life or religious practices of certain populations can influence the development of double monsters.

The etiology of thyroid tumors is a complex subject, complicated by the fact that these tumors are not one entity, but separate neoplasms with different histology, evolution and prognosis. The recognized etiological factors of thyroid cancer include the iodine content of the diet, the inheritance, racial predispositions, the presence of an autoimmune thyroiditis and mostly, the exposure of the thyroid gland to external radiation following radiotherapy. The role played by these factors varies from one type of tumor to another. Thyroid radiation probably represents the most important factor in the development of a papillary carcinoma, with other factors (iodine-rich diet, inheritance, racial predispositions) having a minor role. The follicular carcinoma is more common in regions with low-iodine diet, therefore suggesting that TSH stimulation could be an etiological factor of these tumors. Thyroid radiation may also be carcinogenic for follicular carcinoma although less than for papillary carcinoma. Anaplastic carcinoma appears to originate from a papillary carcinoma already in the thyroid gland. In medullary carcinoma, inheritance plays a major role (autosomal dominant) and lymphomas occur in thyroids already affected by autoimmune thyroiditis. Recent experimental studies have suggested other possible cellular factors as responsible for the development of thyroid tumors. They include an alteration of the responsivity of TSH cellular receptors and the monoclonal mutation of C-cells. These new factors could provide a new insight on the etiology of thyroid tumors

Detailed etiologic and clinico-roentgenological characteristics of pneumoconiosis, as widely spread occupational disease caused by different kinds of dust, are given. The course of pneumoconiosis is discussed depending on working conditions of patients after the disease had been ascertained, as well as its complications, taking into account roentgeno-morphological types of fibrosis and the stages of the disease [ru

Full Text Available Abstract Background Characterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment method designed to produce many such sequences. Results We analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP, and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites. Conclusion We think that training binding site detection

Although soil characteristics modulate metal mobility and bioavailability to organisms, they are often ignored in the risk assessment of metal transfer. This paper aims to determine the ability of chemical methods to assess and predict cadmium (Cd), lead (Pb) and zinc (Zn) environmental bioavailability to the land snail Cantareus aspersus. Snails were exposed in the laboratory for 28 days to 17 soils from around a former smelter. The soils were selected for their range of pH, organic matter, clay content, and Cd, Pb and Zn concentrations. The influence of soil properties on environmental availability (estimated using HF-HClO 4 , EDTA, CaCl 2 , NH 4 NO 3 , NaNO 3 , free ion activity and total dissolved metal concentration in soil solution) and on environmental bioavailability (modelled using accumulation kinetics) was identified. Among the seven chemical methods, only the EDTA and the total soil concentration can be used to assess Cd and Pb environmental bioavailability to snails (r² adj = 0.67 and 0.77, respectively). For Zn, none of the chemical methods were suitable. Taking into account the influence of the soil characteristics (pH and CEC) allows a better prediction of Cd and Pb environmental bioavailability (r² adj = 0.82 and 0.83, respectively). Even though alone none of the chemical methods tested could assess Zn environmental bioavailability to snails, the addition of pH, iron and aluminium oxides allowed the variation of assimilation fluxes to be predicted. A conceptual and practical method to use soil characteristics for risk assessment is proposed based on these results. We conclude that as yet there is no universal chemical method to predict metal environmental bioavailability to snails, and that the soil factors having the greatest impact depend on the metal considered. - Highlights: ► New approach to identify chemical methods able to predict metal bioavailability to snails. ► Bioavailability of cadmium, lead and zinc to snails was determined by

Although soil characteristics modulate metal mobility and bioavailability to organisms, they are often ignored in the risk assessment of metal transfer. This paper aims to determine the ability of chemical methods to assess and predict cadmium (Cd), lead (Pb) and zinc (Zn) environmental bioavailability to the land snail Cantareus aspersus. Snails were exposed in the laboratory for 28 days to 17 soils from around a former smelter. The soils were selected for their range of pH, organic matter, clay content, and Cd, Pb and Zn concentrations. The influence of soil properties on environmental availability (estimated using HF-HClO{sub 4}, EDTA, CaCl{sub 2}, NH{sub 4}NO{sub 3}, NaNO{sub 3}, free ion activity and total dissolved metal concentration in soil solution) and on environmental bioavailability (modelled using accumulation kinetics) was identified. Among the seven chemical methods, only the EDTA and the total soil concentration can be used to assess Cd and Pb environmental bioavailability to snails (r Superscript-Two {sub adj} = 0.67 and 0.77, respectively). For Zn, none of the chemical methods were suitable. Taking into account the influence of the soil characteristics (pH and CEC) allows a better prediction of Cd and Pb environmental bioavailability (r Superscript-Two {sub adj} = 0.82 and 0.83, respectively). Even though alone none of the chemical methods tested could assess Zn environmental bioavailability to snails, the addition of pH, iron and aluminium oxides allowed the variation of assimilation fluxes to be predicted. A conceptual and practical method to use soil characteristics for risk assessment is proposed based on these results. We conclude that as yet there is no universal chemical method to predict metal environmental bioavailability to snails, and that the soil factors having the greatest impact depend on the metal considered. - Highlights: Black-Right-Pointing-Pointer New approach to identify chemical methods able to predict metal bioavailability

The behavior of the yield strength of steel and a number of aluminum alloys is investigated in a wide range of strain rates, based on the incubation time criterion of yield and the empirical models of Johnson-Cook and Cowper-Symonds. In this paper, expressions for the parameters of the empirical models are derived through the characteristics of the incubation time criterion; a satisfactory agreement of these data and experimental results is obtained. The parameters of the empirical models can depend on some strain rate. The independence of the characteristics of the incubation time criterion of yield from the loading history and their connection with the structural and temporal features of the plastic deformation process give advantage of the approach based on the concept of incubation time with respect to empirical models and an effective and convenient equation for determining the yield strength in a wider range of strain rates.

The present work is devoted to investigation of an electrical transmission line with allowance for distributed parameters. From the results of voltage measurements at terminals of an actual transmission line, effective values of the voltage are calculated for every line section. Special attention is given to higher harmonics and asymmetry. Spectral composition of the voltage is presented and changes in values of harmonic components are analyzed. The effect of higher harmonics on the equipment...

Full Text Available The present work is devoted to investigation of an electrical transmission line with allowance for distributed parameters. From the results of voltage measurements at terminals of an actual transmission line, effective values of the voltage are calculated for every line section. Special attention is given to higher harmonics and asymmetry. Spectral composition of the voltage is presented and changes in values of harmonic components are analyzed. The effect of higher harmonics on the equipment operation is analyzed.

The working capacity of any device primarily depends on the assembly accuracy which, in its turn, is determined by the quality of each part manufactured, i.e., the degree of conformity between final geometrical parameters and the set ones. However, the assembly accuracy depends not only on a qualitative manufacturing process but also on the assembly process correctness. In this connection, there were preliminary calculations of assembly stages in terms of conformity to real geometrical parameters with their permissible values. This task is performed by means of the calculation of dimensional chains. The calculation of interrelated dimensional chains in the aircraft industry requires particular attention. The article considers the issues of dimensional chain calculation modelling by the example of the turbine wheel assembly process. The authors described the solution algorithm in terms of mathematical statistics implemented in Matlab. The paper demonstrated the results of a dimensional chain calculation for a turbine wheel in relation to the draw of turbine blades to the shroud ring diameter. Besides, the article provides the information on the influence of a geometrical parameter tolerance for the dimensional chain link elements on a closing one.

Pilot study to analyse the efficacy and embryo morphology using a new human embryo culture medium (GM501) versus the conventional used medium (ISM1). Over a four-month period, all patients at the Leuven Institute of Fertility and Embryology (LIFE) were -randomly allocated to have their embryos cultured in either the standard sequential culture medium ISM1 (control) or in a new universal medium (GM501) (study group). Primary outcome parameters were clinical pregnancy and live birth rate. The secondary outcome parameter was the correlation of embryo fragmentation rate with pregnancy outcome. We did not observe any differences between the ISM1 control group and GM501 study group with regard to fertilization, pregnancy, implantation rates, ongoing pregnancy, and babies born. The number of embryos with a minimal fragmentation rate (less than 30%) was significantly higher in the GM501 study group. Although a significant higher embryo fragmentation rate was seen in In vitro culture of embryos in GM501, pregnancy outcome results were comparable to those of embryos cultured in ISM1. According to our results the value of embryo morphological criteria as a parameter for pregnancy outcome should be examined and discussed again.

Three potential predictive assays of the repopulation component in tumour response to therapy are considered. (1) The DNA index can easily be measured. It is of prognostic value for cancers of certain sites, aneuploidy being a bad prognostic indicator. It is not strictly an indicator of cell proliferation. (2) The in vitro labelling index is of predictive value in early stage operable breast cancer and in head and neck cancer. In the former a high pretreatment labelling index can identify patients who could benefit from adjuvant chemotherapy. (3) The tumour potential doubling time (Tpot) can be measured rapidly following in vivo labelling with bromodeoxyuridine or iododeoxyuridine. We have measured Tpot in over 100 solid tumours with a success rate of about 75 per cent. Nearly 50 per cent of the tumours have a pre-treatment potential doubling time of 5 days or less. These would be suitable candidates for accelerated fractionation.

Three potential predictive assays of the repopulation component in tumour response to therapy are considered. (1) The DNA index can easily be measured. It is of prognostic value for cancers of certain sites, aneuploidy being a bad prognostic indicator. It is not strictly an indicator of cell proliferation. (2) The in vitro labelling index is of predictive value in early stage operable breast cancer and in head and neck cancer. In the former a high pretreatment labelling index can identify patients who could benefit from adjuvant chemotherapy. (3) The tumour potential doubling time can be measured rapidly following in vivo labelling with bromodeoxyuridine or iododeoxyuridine. The authors measured T pot in over 100 solid tumours with a success rate of about 75%. Nearly 50% of the tumours have a pre-treatment potential doubling time of 5 days or less. These would be suitable candidates for accelerated fractionation. (author)

Vegetative filter strips (VFS) are an environmental management tool used to reduce sediment and pesticide transport from surface runoff. Numerical models of VFS such as the Vegetative Filter Strip Modeling System (VFSMOD-W) are capable of predicting runoff, sediment, and pesticide reduction and can be useful tools to understand the effectiveness of VFS and environmental conditions under which they may be ineffective. However, as part of the modeling process, it is critical to identify input factor importance and quantify uncertainty in predicted runoff, sediment, and pesticide reductions. This research used state-of-the-art global sensitivity and uncertainty analysis tools, a screening method (Morris) and a variance-based method (extended Fourier Analysis Sensitivity Test), to evaluate VFSMOD-W under a range of field scenarios. The three VFS studies analyzed were conducted on silty clay loam and silt loam soils under uniform, sheet flow conditions and included atrazine, chlorpyrifos, cyanazine, metolachlor, pendimethalin, and terbuthylazine data. Saturated hydraulic conductivity was the most important input factor for predicting infiltration and runoff, explaining >75% of the total output variance for studies with smaller hydraulic loading rates ( approximately 100-150 mm equivalent depths) and approximately 50% for the higher loading rate ( approximately 280-mm equivalent depth). Important input factors for predicting sedimentation included hydraulic conductivity, average particle size, and the filter's Manning's roughness coefficient. Input factor importance for pesticide trapping was controlled by infiltration and, therefore, hydraulic conductivity. Global uncertainty analyses suggested a wide range of reductions for runoff (95% confidence intervals of 7-93%), sediment (84-100%), and pesticide (43-100%) . Pesticide trapping probability distributions fell between runoff and sediment reduction distributions as a function of the pesticides' sorption. Seemingly

Full Text Available Atmospheric drag force is an important source of perturbation of Low Earth Orbit (LEO orbit satellites, and solar activity is a major factor for changes in atmospheric density. In particular, the orbital lifetime of a satellite varies with changes in solar activity, so care must be taken in predicting the remaining orbital lifetime during preparation for post-mission disposal. In this paper, the System Tool Kit (STK® Long-term Orbit Propagator is used to analyze the changes in orbital lifetime predictions with respect to solar activity. In addition, the STK® Lifetime tool is used to analyze the change in orbital lifetime with respect to solar flux data generation, which is needed for the orbital lifetime calculation, and its control on the drag coefficient control. Analysis showed that the application of the most recent solar flux file within the Lifetime tool gives a predicted trend that is closest to the actual orbit. We also examine the effect of the drag coefficient, by performing a comparative analysis between varying and constant coefficients in terms of solar activity intensities.

The molecular structure of methanediol has been investigated by means of quantum chemical calculations. Two conformers, corresponding to C{sub 2} and C {sub s} symmetries, respectively, were considered. The C{sub 2} conformer is found to lie about 1.7 (at 298 K) or 2.3 (at 0 K) kcal mol{sup –1} below the C {sub s} conformer. Predictions for their rotational constants, vibrational frequencies, IR intensities, and dipole moments have been provided. The lowest-lying isomer has a very low dipole moment, around 0.03 D, whereas the C {sub s} conformer has a relatively high dipole moment, namely, 2.7 D. The barrier for the C {sub s} →C{sub 2} process is predicted to be around 0.7-1 kcal mol{sup –1}. Based on the energetic results the proportion of the C{sub s} conformer is likely to be negligible under low temperature conditions, such as in the interstellar medium. Therefore, it is predicted that detection by radioastronomy of methanediol would be rather unlikely.

Duplex stainless steels are commonly used (among others in nuclear industry) for their good properties. However these steels may 'age' in service condition at high temperatures. As their mechanical properties (Charpy impact toughness, resistance to ductile tearing) are often very scattered and tend to decrease after ageing, it has become essential to predict them with high precision. For this, we propose to explain a part of the scattering of the mechanical properties with measurable parameters in relation with the particularly complicated two-phase morphology. The two-phase and bi-percolated morphology of the ferrite and austenite phases is first characterised from the observation of 2D images and from the reconstitution of a 3D image. At the same time we precise the genesis of the formation's mechanisms of the structure (germination and growth of the austenitic phase in the solidified ferri tic one) in relation with the literature. The morphological characteristics so observed corresponding with classical notions of mathematical morphology, - size, covariance, connexity -, we use morphological operators to measure morphological variables by image analysis. We establish then a link between toughness and a parameter measuring fineness of the morphology. The lack of data for very aged steels prevent us from proposing a model of toughness which could take this parameter into consideration at these ageing states, for which it is properly the more crucial to obtain specially precise predictions. A mathematical mo del of the 3D structure of the steel is finally proposed. We choose an homogeneous Markov chain of 3D spatial processes, whose evolution in time mimes the solidification. The morphology of the microstructure is so summarised with 8 parameters. This model is liable to be coupled with a model of toughness, for which it would so enlarge the possibilities of prediction. It could also be used to simulate subsequently the damage and the rupture of two

Fatty liver syndrome and ketosis are important metabolic disorders in high-producing cows during early lactation with fatty liver usually preceding ketosis. To date, parameters for early prediction of the risk of ketosis have not been investigated in China. To determine the predictive value of some parameters on the risk of ketosis in China. In a descriptive study, 48 control and 32 ketotic Holstein Friesian cows were randomly selected from one farm with a serum β-hydroxybutyrate (BHBA) concentration of 1.20 mmol/L as cutoff point. The risk prediction thresholds for ketosis were determined by receiver operating characteristic (ROC) analysis. In line with a high BHBA concentration, blood glucose concentration was significantly lower in ketotic cows compared to control animals (2.77 ± 0.24 versus 3.34 ± 0.03 mmol/L; P = 0.02). Thresholds were more than 0.76 mmol/L for nonesterified fatty acids (NEFA, with 65% sensitivity and 92% specificity), more than 104 U/L for aspartate aminotransferase (AST, 74% and 85%, respectively), less than 140 U/L for cholinesterase (CHE, 75% and 59%, respectively), and more than 3.3 µmol/L for total bilirubin (TBIL, 58% and 83%, respectively). There were significant correlations between BHBA and glucose (R = -4.74), or CHE (R = -0.262), BHBA and NEFA (R = 0.520), or AST (R = 0.525), or TBIL (R = 0.278), or direct bilirubin (DBIL, R = 0.348). AST, CHE, TBIL and NEFA may be useful parameters for risk prediction of ketosis. This study might be of value in addressing novel directions for future research on the connection between ketosis and liver dysfunction.

The aim of this study was to determine whether diffusion and perfusion imaging parameters demonstrate different diagnostic values for predicting pseudoprogression between glioblastoma subgroups stratified by O{sup 6}-mythylguanine-DNA methyltransferase (MGMT) promoter methylation status. We enrolled seventy-five glioblastoma patients that had presented with enlarged contrast-enhanced lesions on magnetic resonance imaging (MRI) one month after completing concurrent chemoradiotherapy and undergoing MGMT promoter methylation testing. The imaging parameters included 10 or 90 % histogram cutoffs of apparent diffusion coefficient (ADC10), normalized cerebral blood volume (nCBV90), and initial area under the time signal-intensity curve (IAUC90). The results of the areas under the receiver operating characteristic curve (AUCs) with cross-validation were compared between MGMT methylation and unmethylation groups. MR imaging parameters demonstrated a trend toward higher accuracy in the MGMT promoter methylation group than in the unmethylation group (cross-validated AUCs = 0.70-0.95 and 0.56-0.87, respectively). The combination of MGMT methylation status with imaging parameters improved the AUCs from 0.70 to 0.75-0.90 for both readers in comparison with MGMT methylation status alone. The probability of pseudoprogression was highest (95.7 %) when nCBV90 was below 4.02 in the MGMT promoter methylation group. MR imaging parameters could be stronger predictors of pseudoprogression in glioblastoma patients with the methylated MGMT promoter than in patients with the unmethylated MGMT promoter. (orig.)

Full Text Available In an earlier study, Thampi et al. (2006 have shown that the strength and asymmetry of Equatorial Ionization Anomaly (EIA, obtained well ahead of the onset time of Equatorial Spread F (ESF have a definite role on the subsequent ESF activity, and a new "forecast parameter" has been identified for the prediction of ESF. This paper presents the observations of EIA strength and asymmetry from the Indian longitudes during the period from August 2005–March 2007. These observations are made using the line of sight Total Electron Content (TEC measured by a ground-based beacon receiver located at Trivandrum (8.5° N, 77° E, 0.5° N dip lat in India. It is seen that the seasonal variability of EIA strength and asymmetry are manifested in the latitudinal gradients obtained using the relative TEC measurements. As a consequence, the "forecast parameter" also displays a definite seasonal pattern. The seasonal variability of the EIA strength and asymmetry, and the "forecast parameter" are discussed in the present paper and a critical value for has been identified for each month/season. The likely "skill factor" of the new parameter is assessed using the data for a total of 122 days, and it is seen that when the estimated value of the "forecast parameter" exceeds the critical value, the ESF is seen to occur on more than 95% of cases.

Diabetic foot is a common complication of diabetes due to changes in blood vessels and nerves, often leads to ulceration and subsequent limb amputation if not treated early. A new diagnostic marker of bacterial infections is procalcitonin. C-reactive protein, Interleukin1β, Interleukin-6 and tumor necrosis factor-α as proinflammatory parameters increased in Diabetic foot infection. We evaluated above parameters in patients with diabetic foot infections in different grades. A total of 130 diabetic patients were enrolled in this case control study between June 2011 and March 2012 in Rizgary, Emergency and Hawler Teaching Hospitals, 90 of them with diabetic foot lesion as a patient group. 40 without foot lesion, as a patient control and 20 individuals as healthy control. Assessment of above parameters in sera of study groups and also bacteriological tests (bacterial isolation and identification) were done. Serum procalcitonin levels significantly increased in patients with diabetic foot with higher Wagner grades (III, IV and V) (0.28 ± 0.04, 0.30 ± 0.07 and 0.60 ± 0.11) respectively (Pfoot ulcer based on Wagner classification system was also associated with circulating levels of C-reactive protein, Interleukin1β, Interleukin-6 and tumor necrosis factor-α (G III, IV and V) (5.36 ± 0.70, 6.38 ± 0.65, and 9.13 ± 0.88), (1.21 ± 0.08, 1.56 ± 0.16 and 2.02 ± 0.07), (23.02 ± 2.98, 36.32 ± 5.75 and 43.36 ± 6.16), and (215.39 ± 16.8, 259.21 ± 40.7 and 398.45 ± 33.4) respectively (Pdiabetic foot patients may be a procalcitonin especially in those with higher Wagner grades and with polymicrobial infection.

Coke oven gas treatment plants are well equipped with distributed control systems (DCS) and therefore recording the vast amount of operational data efficiently. Analyzing the stored information manually from historians is practically impossible. In this study, data mining technique was examined for lowering the ammonia concentration in clean coke oven gas. Results confirm that concentration of ammonia in clean coke oven gas depends on the average PCDC temperature; gas scrubber temperatures stripped liquor flow, stripped liquor concentration and stripped liquor temperature. The optimum operating ranges of the above dependent parameters using data mining technique for lowering the concentration of ammonia is described in this paper.

Aims: We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in this case) to the given inputs (spectra). Such a model can be integrated in a Bayesian framework to estimate the posterior distribution of the outputs. Methods: The architecture of the GANN follows the same scheme as a normal ANN, but with the inputs and outputs inverted. We train the network with the set of atmospheric parameters (Teff, log g, [Fe/H] and [α/ Fe]), obtaining the stellar spectra for such inputs. The residuals between the spectra in the grid and the estimated spectra are minimized using a validation dataset to keep solutions as general as possible. Results: The performance of both conventional ANNs and GANNs to estimate the stellar parameters as a function of the star brightness is presented and compared for different Galactic populations. GANNs provide significantly improved parameterizations for early and intermediate spectral types with rich and intermediate metallicities. The behaviour of both algorithms is very similar for our sample of late-type stars, obtaining residuals in the derivation of [Fe/H] and [α/ Fe] below 0.1 dex for stars with Gaia magnitude Grvs satellite. Conclusions: Uncertainty estimation of computed astrophysical parameters is crucial for the validation of the parameterization itself and for the subsequent exploitation by the astronomical community. GANNs produce not only the parameters for a given spectrum, but a goodness-of-fit between the observed spectrum and the predicted one for a given set of parameters. Moreover, they allow us to obtain the full posterior distribution over the astrophysical parameters space once a noise model is assumed. This can be used for novelty detection and quality assessment.

Full Text Available IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN, Wavelet Neural Network combination (WANN and multi linear regression (MLR models, to predict next month the Nitrate (NO3 and Chloride (CL ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated. Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL has been put related to the past amounts of the same time series (NO3 / CL and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data as training set, next 10% of data (12 months and 10% of the end of time series (terminal 12 months were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q and mentioned qualityparameters(NO3 / CL were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E and the Root Mean Square

Full Text Available This study was conducted to assess the influences of soil surface conditions and initial soil water content on water movement in unsaturated sodic soils of reclaimed coastal areas. Data was collected from column experiments in which two soils from a Chinese coastal area reclaimed in 2007 (Soil A, saline and 1960 (Soil B, nonsaline were used, with bulk densities of 1.4 or 1.5 g/cm3. A 1D-infiltration model was created using a finite difference method and its sensitivity to hydraulic related parameters was tested. The model well simulated the measured data. The results revealed that soil compaction notably affected the water retention of both soils. Model simulations showed that increasing the ponded water depth had little effect on the infiltration process, since the increases in cumulative infiltration and wetting front advancement rate were small. However, the wetting front advancement rate increased and the cumulative infiltration decreased to a greater extent when θ0 was increased. Soil physical quality was described better by the S parameter than by the saturated hydraulic conductivity since the latter was also affected by the physical chemical effects on clay swelling occurring in the presence of different levels of electrolytes in the soil solutions of the two soils.

This study was conducted to assess the influences of soil surface conditions and initial soil water content on water movement in unsaturated sodic soils of reclaimed coastal areas. Data was collected from column experiments in which two soils from a Chinese coastal area reclaimed in 2007 (Soil A, saline) and 1960 (Soil B, nonsaline) were used, with bulk densities of 1.4 or 1.5 g/cm(3). A 1D-infiltration model was created using a finite difference method and its sensitivity to hydraulic related parameters was tested. The model well simulated the measured data. The results revealed that soil compaction notably affected the water retention of both soils. Model simulations showed that increasing the ponded water depth had little effect on the infiltration process, since the increases in cumulative infiltration and wetting front advancement rate were small. However, the wetting front advancement rate increased and the cumulative infiltration decreased to a greater extent when θ₀ was increased. Soil physical quality was described better by the S parameter than by the saturated hydraulic conductivity since the latter was also affected by the physical chemical effects on clay swelling occurring in the presence of different levels of electrolytes in the soil solutions of the two soils.

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time. PMID:29629209

Full Text Available Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10, 50% (T50, and 85% (T85 recovery; cetane index; and biodiesel content through attenuated total reflection Fourier transform infrared (ATR-FTIR spectroscopy and the multivariate regression method, partial least square (PLS. For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW and orthogonal signal correction (OSC, was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS, backward interval PLS (BiPLS, and genetic algorithm (GA. The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm -1 . The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.

This study aimed at identifying prior therapy dosimetric parameters using 99m Tc-labeled macro-aggregates of albumin (MAA) that are associated with contralateral hepatic hypertrophy occurring after unilobar radioembolization of hepatocellular carcinoma (HCC) performed with 90 Y-loaded glass microspheres. The dosimetry data of 73 HCC patients were collected prior to the treatment with 90 Y-loaded microspheres for unilateral disease. The injected liver dose (ILD), the tumor dose (TD) and healthy injected liver dose (HILD) were calculated based on MAA quantification. Following treatment, the maximal hypertrophy (MHT) of an untreated lobe was calculated. Mean MHT was 35.4 ± 40.4%. When using continuous variables, the MHT was not correlated with any tested variable, i.e., injected activity, ILD, HILD or TD except with a percentage of future remnant liver (FRL) following the 90 Y-microspheres injection (r = -0.56). MHT ≥ 10% was significantly more frequent for patients with HILD ≥ 88 Gy, (52% of the cases), i.e., in 92.2% versus 65.7% for HILD < 88 Gy (p = 0.032). MHT ≥ 10% was also significantly more frequent for patients with a TD ≥ 205 Gy and a tumor volume (VT) ≥ 100 cm 3 in patients with initial FRL < 50%. MHT ≥10% was seen in 83.9% for patients with either an HILD ≥ 88 Gy or a TD ≥ 205 Gy for tumors larger than 100cm 3 (85% of the cases), versus only 54.5% (p = 0.0265) for patients with none of those parameters. MHT ≥10% was also associated with FRL and the Child-Pugh score. Using multivariate analysis, the Child-Pugh score (p < 0.0001), FRL (p = 0.0023) and HILD (p = 0.0029) were still significantly associated with MHT ≥10%. This study demonstrates for the first time that HILD is significantly associated with liver hypertrophy. There is also an impact of high tumor doses in large lesions in one subgroup of patients. Larger prospective studies evaluating the MAA dosimetric parameters have to be conducted to confirm these promising results

This study aimed at identifying prior therapy dosimetric parameters using {sup 99m}Tc-labeled macro-aggregates of albumin (MAA) that are associated with contralateral hepatic hypertrophy occurring after unilobar radioembolization of hepatocellular carcinoma (HCC) performed with {sup 90}Y-loaded glass microspheres. The dosimetry data of 73 HCC patients were collected prior to the treatment with {sup 90}Y-loaded microspheres for unilateral disease. The injected liver dose (ILD), the tumor dose (TD) and healthy injected liver dose (HILD) were calculated based on MAA quantification. Following treatment, the maximal hypertrophy (MHT) of an untreated lobe was calculated. Mean MHT was 35.4 ± 40.4%. When using continuous variables, the MHT was not correlated with any tested variable, i.e., injected activity, ILD, HILD or TD except with a percentage of future remnant liver (FRL) following the {sup 90}Y-microspheres injection (r = -0.56). MHT ≥ 10% was significantly more frequent for patients with HILD ≥ 88 Gy, (52% of the cases), i.e., in 92.2% versus 65.7% for HILD < 88 Gy (p = 0.032). MHT ≥ 10% was also significantly more frequent for patients with a TD ≥ 205 Gy and a tumor volume (VT) ≥ 100 cm{sup 3} in patients with initial FRL < 50%. MHT ≥10% was seen in 83.9% for patients with either an HILD ≥ 88 Gy or a TD ≥ 205 Gy for tumors larger than 100cm{sup 3} (85% of the cases), versus only 54.5% (p = 0.0265) for patients with none of those parameters. MHT ≥10% was also associated with FRL and the Child-Pugh score. Using multivariate analysis, the Child-Pugh score (p < 0.0001), FRL (p = 0.0023) and HILD (p = 0.0029) were still significantly associated with MHT ≥10%. This study demonstrates for the first time that HILD is significantly associated with liver hypertrophy. There is also an impact of high tumor doses in large lesions in one subgroup of patients. Larger prospective studies evaluating the MAA dosimetric parameters have to be conducted to confirm

Broadband ground motions are estimated in the Kanto sedimentary basin which holds Tokyo metropolitan area inside for anticipated great interplate earthquakes along surrounding plate boundaries. Possible scenarios of great earthquakes along Sagami trough are modeled combining characteristic properties of the source area and adequate variation in source parameters in order to evaluate possible ground motion variation due to next Kanto earthquake. South to the rupture area of the 2011 Tohoku earthquake along the Japan trench, we consider possible M8 earthquake. The ground motions are computed with a four-step hybrid technique. We first calculate low-frequency ground motions at the engineering basement. We then calculate higher-frequency ground motions at the same position, and combine the lower- and higher-frequency motions using a matched filter. We finally calculate ground motions at the surface by computing the response of the alluvium-diluvium layers to the combined motions at the engineering basement.

Solid-liquid separation of animal slurry, with solid fractions used for composting, has gained interest recently. However, efficient composting of separated animal slurry solid fractions (SSFs) requires a better understanding of the process dynamics in terms of important physical parameters...... and their interacting physical relationships in the composting matrix. Here we monitored moisture content, bulk density, particle density and air-filled porosity (AFP) during composting of SSF collected from four commercially available solid-liquid separators. Composting was performed in laboratory-scale reactors...... for 30 days (d) under forced aeration and measurements were conducted on the solid samples at the beginning of composting and at 10-d intervals during composting. The results suggest that differences in initial physical properties of SSF influence the development of compost maximum temperatures (40...

This paper deals with the computational simulation of a wood waste (sawdust) gasifier using an equilibrium model based on minimization of the Gibbs free energy. The gasifier has been tested with Pinus Elliotis sawdust, an exotic specie largely cultivated in the South of Brazil. The biomass used in the tests presented a moisture of nearly 10% (wt% on wet basis), and the average composition results of the gas produced (without tar) are compared with the equilibrium models used. Sensitivity studies to verify the influence of the moisture sawdust content on the fuel gas composition and on its heating value were made. More complex models to reproduce with better accuracy the gasifier studied were elaborated. Although the equilibrium models do not represent the reactions that occur at relatively high temperatures ( {approx_equal} 800 deg C) very well, these models can be useful to show some tendencies on the working parameter variations of a gasifier. (Author)

Full Text Available The paper describes the verification of casting and solidification of heavy slab ingot weighing 40 t from tool steel by means of numerical modelling with use of a finite element method. The pre-processing, processing and post-processing phases of numerical modelling are outlined. Also, the problems with determination of the thermodynamic properties of materials and with determination of the heat transfer between the individual parts of the casting system are discussed. The final porosity, macrosegregation and the risk of cracks were predicted. The results allowed us to use the slab ingot instead of the conventional heavy steel ingot and to improve the ratio, the chamfer and the external shape of the wall of the new design of the slab ingot.

Interest in and demand for preserved seafood with reduced salt/sodium content is increasing. As a consequence of the reduced salt content potential growth of psychrotolerant pseudomonads to unacceptable high concentration where they cause product spoilage is an increasing challenge. Innovation...... include the effect of temperatures and salt. However, these simple secondary models do not include the effect of a broader range of product characteristics and therefore they cannot be used to predict how the inhibiting effect of salt can be replaced by changes in other environmental factors The objective...... and including terms for temperature, pH, aw/NaCl, lactic- and sorbic acids (Martinez-Rios et al., Int. J. Food Microbiol. 216. 110-120, 2016). MIC-values for acetic-, benzoic- and citric acids were determined in broth and terms modelling their antimicrobial effect were added to the model. The new and expanded...

Regulatory limits on cadmium (Cd) content in food products are tending to become stricter, especially in cereals, which are a major contributor to dietary intake of Cd by humans. This is of particular importance for durum wheat, which accumulates more Cd than bread wheat. The contamination of durum wheat grain by Cd depends not only on the genotype but also to a large extent on soil Cd availability. Assessing the phytoavailability of Cd for durum wheat is thus crucial, and appropriate methods are required. For this purpose, we propose a statistical model to predict Cd accumulation in durum wheat grain based on soil geochemical properties related to Cd availability in French agricultural soils with low Cd contents and neutral to alkaline pH (soils commonly used to grow durum wheat). The best model is based on the concentration of total Cd in the soil solution, the pH of a soil CaCl 2 extract, the cation exchange capacity (CEC), and the content of manganese oxides (Tamm's extraction) in the soil. The model variables suggest a major influence of cadmium buffering power of the soil and of Cd speciation in solution. The model successfully explains 88% of Cd variability in grains with, generally, below 0.02 mg Cd kg -1 prediction error in wheat grain. Monte Carlo cross-validation indicated that model accuracy will suffice for the European Community project to reduce the regulatory limit from 0.2 to 0.15 mg Cd kg -1 grain, but not for the intermediate step at 0.175 mg Cd kg -1 . The model will help farmers assess the risk that the Cd content of their durum wheat grain will exceed regulatory limits, and help food safety authorities test different regulatory thresholds to find a trade-off between food safety and the negative impact a too strict regulation could have on farmers.

Etiological diagnosis of leg ulcers must be the first step of treatment, even if we know that veinous disease is often present. We can build a clinical decisional diagram, which helps us to understand and not forget the other causes of chronic wounds and choose some basic examination, like ultrasound and histological findings. This diagnosis helps to choose the right treatment in order to cure even the oldest venous ulcers. Educational programs should be improved to prevent recurrence.

Hyperthyroidism is a common endocrine disorder in young women of childbearing age. Approximately one to three cases of gestational hyperthyroidism occur per 1000 pregnancies. All etiologies of hyperthyroidism may be encountered during pregnancy but they are dominated by Graves\\\\\\' disease and gestational transient thyrotoxicosis. The first requires an antithyroid drug treatment and the second progresses well under symptomatic treatment. Hence the interest of the Establishment of the cause of ...

Full Text Available Context: Sudden cardiac death is one of the leading causes of death in Europe, and early prognostication remains challenging. There is a lack of valid parameters for the prediction of survival after cardiac arrest. Aims: This study aims to investigate if arterial blood gas parameters correlate with mortality of patients after out-of-hospital cardiac arrest. Materials and Methods: All patients who were admitted to our hospital after resuscitation following out-of-hospital cardiac arrest between January 1, 2008, and December 31, 2013, were included in this retrospective study. The patient's survival 5 days after resuscitation defined the study end-point. For the statistical analysis, the mean, standard deviation, Student's t-test, Chi-square test, and logistic regression analyses were used (level of significance P< 0.05. Results: Arterial blood gas samples were taken from 170 patients. In particular, pH < 7.0 (odds ratio [OR]: 7.20; 95% confidence interval [CI]: 3.11–16.69; P< 0.001 and lactate ≥ 5.0 mmol/L (OR: 6.79; 95% CI: 2.77–16.66; P< 0.001 showed strong and independent correlations with mortality within the first 5 days after hospital admission. Conclusion: Our study results indicate that several arterial blood gas parameters correlate with mortality of patients after out-of-hospital resuscitation. The most relevant parameters are pH and lactate because they are strongly and independently associated with mortality within the first 5 days after resuscitation. Despite this correlation, none of these parameters by oneself is strong enough to allow an early prognostication. Still, these parameters can contribute as part of a multimodal approach to assessing the patients' prognosis.

Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test, resulting in p value and relative risk maps of all percentile–time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile–time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee. Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile–time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile–time interval of nRSI was associated with progression-free survival. Conclusions: The percentile–time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer.

This paper studies the use of adaptive Support Vector Machine (SVM) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For SVM modelling, different values for radial basis function (RBF) kernel width and penalty parameters (C) were considered and the optimum values were then found. The results demonstrate that SVM is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve complete combustion of the fuel and reduce the exhaust emissions significantly.

Full Text Available Preeclampsia (PE is an obstetric disorder with high morbidity and mortality rates but without clear pathogeny. The dysfunction of the blood coagulation-fibrinolysis system is a salient characteristic of PE that varies in severity, and necessitates different treatments. Therefore, it is necessary to find suitable predictors for the onset and severity of PE.We aimed to evaluate blood coagulation parameters and platelet indices as potential predictors for the onset and severity of PE.Blood samples from 3 groups of subjects, normal pregnant women (n = 79, mild preeclampsia (mPE (n = 53 and severe preeclampsia (sPE (n = 42, were collected during early and late pregnancy. The levels of coagulative parameters and platelet indices were measured and compared among the groups. The receiver-operating characteristic (ROC curves of these indices were generated, and the area under the curve (AUC was calculated. The predictive values of the selected potential parameters were examined in binary regression analysis.During late pregnancy in the normal pregnancy group, the activated partial thromboplastin time (APTT, prothrombin time (PT, thrombin time (TT and platelet count decreased, while the fibrinogen level and mean platelet volume (MPV increased compared to early pregnancy (p<0.05. However, the PE patients presented with increased APTT, TT, MPV and D-dimer (DD during the third trimester. In the analysis of subjects with and without PE, TT showed the largest AUC (0.743 and high predictive value. In PE patients with different severities, MPV showed the largest AUC (0.671 and ideal predictive efficiency.Normal pregnancy causes a maternal physiological hypercoagulable state in late pregnancy. PE may trigger complex disorders in the endogenous coagulative pathways and consume platelets and FIB, subsequently activating thrombopoiesis and fibrinolysis. Thrombin time and MPV may serve as early monitoring markers for the onset and severity of PE

In this study, osmotic coefficients and water activities in aqueous solutions have been modeled using a new approach based on the Pitzer model. This model contains two physically significant ionic parameters regarding ionic solvation and the closest distance of approach between ions in a solution. The proposed model was evaluated by estimating the osmotic coefficients of nine electrolytes in aqueous solutions. The obtained results showed that the model is suitable for predicting the osmotic coefficients in aqueous electrolyte solutions. Using adjustable parameters, which have been calculated from regression between the experimental osmotic coefficient and the results of this model, the water activity coefficients of aqueous solutions were calculated. The average absolute relative deviations of the osmotic coefficients between the experimental data and the calculated results were in agreement

Chronic diarrhea is a common syndrome. An etiological diagnosis is often reached through clinical history, physical examination and simple tests. In some cases, when the etiology is not found, the syndrome is called functional diarrhea, even though established criteria are often not fulfilled. We present the case of a patient with diarrhea for several months. The most common causes were ruled out through clinical history, physical examination, radiographic studies and laboratory tests, and the patient was diagnosed with functional diarrhea. Three months later, the patient presented a neck mass, and biopsy revealed medullary carcinoma of the thyroid. A review of recommendations for the systematic evaluation of chronic diarrhea is presented. A general approach should include careful history taking characteristics of diarrhea (onset, associated symptoms, epidemiological factors, iatrogenic causes such as laxative ingestion), a thorough physical examination with special attention to the anorectal region, and routine laboratory tests (complete blood count and serum chemistry). In addition, stool analysis including electrolytes (fecal osmotic gap), leukocytes, fecal occult blood, excess stool fat and laxative screening can yield important objective information to classify the diarrhea as: osmotic (osmotic gaps > 125 mOsm/Kg), secretory (osmotic gaps diarrhea described above. A systematic approach to the evaluation of chronic diarrhea is warranted. Medullary thyroid carcinoma and other endocrine syndromes causing chronic diarrhea are very rare. Measurement of serum peptide concentrations should only be performed when clinical presentation and findings in stool or radiographic studies suggest this etiology.

This retrospective study aimed to identify novel pre-stimulation parameters associated with live birth in IVF and to develop a model for prediction of the chances of live birth at an early phase of the treatment cycle. Data were collected from a randomized trial in couples with unexplained...... infertility, tubal factor, mild male factor or other reason for infertility. All women (n=731) had undergone an IVF cycle (no intracytoplasmic sperm injection) after stimulation with human menopausal gonadotrophin or follicle-stimulating hormone following the long gonadotrophin-releasing hormone agonist...

infertility, tubal factor, mild male factor or other reason for infertility. All women (n=731) had undergone an IVF cycle (no intracytoplasmic sperm injection) after stimulation with human menopausal gonadotrophin or follicle-stimulating hormone following the long gonadotrophin-releasing hormone agonist......This retrospective study aimed to identify novel pre-stimulation parameters associated with live birth in IVF and to develop a model for prediction of the chances of live birth at an early phase of the treatment cycle. Data were collected from a randomized trial in couples with unexplained...

{sup 18}F-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is widely used to diagnose and stage non-small cell lung cancer (NSCLC). The aim of this retrospective study was to evaluate the predictive ability of different FDG standardized uptake values (SUVs) in 74 patients with newly diagnosed NSCLC. {sup 18}F-FDG PET/CT scans were performed and different SUV parameters (SUV{sub max}, SUV{sub avg}, SUV{sub T/L}, and SUV{sub T/A}) obtained, and their relationship with clinical characteristics were investigated. Meanwhile, correlation and multiple stepwise regression analyses were performed to determine the primary predictor of SUVs for NSCLC. Age, gender, and tumor size significantly affected SUV parameters. The mean SUVs of squamous cell carcinoma were higher than those of adenocarcinoma. Poorly differentiated tumors exhibited higher SUVs than well-differentiated ones. Further analyses based on the pathologic type revealed that the SUV{sub max}, SUV{sub avg}, and SUV{sub T/L} of poorly differentiated adenocarcinoma tumors were higher than those of moderately or well-differentiated tumors. Among these four SUV parameters, SUV{sub T/L} was the primary predictor for tumor differentiation. However, in adenocarcinoma, SUV{sub max} was the determining factor for tumor differentiation. Our results showed that these four SUV parameters had predictive significance related to NSCLC tumor differentiation; SUV{sub T/L} appeared to be most useful overall, but SUV{sub max} was the best index for adenocarcinoma tumor differentiation.

PURPOSE: Bleeding volume in orthognathic surgery (OS) varies considerably, although OS comprises standardized procedures and the patient population consists of young healthy individuals. The aim of this prospective cohort study was to investigate the influence of preoperative sex-related differen......PURPOSE: Bleeding volume in orthognathic surgery (OS) varies considerably, although OS comprises standardized procedures and the patient population consists of young healthy individuals. The aim of this prospective cohort study was to investigate the influence of preoperative sex......-related differences in hemostatic parameters on intraoperative bleeding (IOB) volume in OS. MATERIALS AND METHODS: Patients scheduled for routine OS in our department in Esbjerg, Denmark, were included as study patients in this short-term cohort study. The primary predictor variable was patient sex, and the primary...... the χ(2) test, Mann-Whitney U test, Pearson product moment correlation analysis, and analysis of covariance for analyses of dichotomous variables, comparison between sex, correlations between IOB volume and secondary predictors, and adjustment for confounders, respectively. RESULTS: Forty...

Full Text Available http://dx.doi.org/10.5902/198050989297A Bayesian analysis of genetic parameters for growth traits at twelve months after planting was carried out in twenty nine Eucalyptus globulus clones in southern Chile. Two different environmental conditions were considered: 1 Non-irrigation and; 2 Plants were irrigated with a localized irrigation system. The Bayesian approach was performed using Gibbs sampling algorithm in a clone-environment interaction model. Inheritability values ​​were high in the water supply condition (posterior mode: H2=0.41, 0.36 and 0.39 for height, diameter and sectional area, respectively, while in the environment without irrigation, the inheritabilities were significantly lower, which was confirmed by the Bayesian credible intervals (95% probability. The posterior mode of the genetic correlation between sites was positive and high for all traits (r=0.7, 0.65 and 0.8, for height, diameter and sectional area, respectively and according to the credible interval, it was statistically different from zero, indicating a non-significant interaction.

Full Text Available This study handles artificial neural networks (ANN modeling to predict tire contact area and rolling resistance due to the complex and nonlinear interactions between soil and wheel that mathematical, numerical and conventional models fail to investigate multivariate input and output relationships with nonlinear and complex characteristics. Experimental data acquisitioning was carried out using a soil bin facility with single-wheel tester at seven inflation pressures of tire (i.e. 100–700 kPa and seven different wheel loads (1–7 KN with two soil textures and two tire types. The experimental datasets were used to develop a feed-forward with back propagation ANN model. Four criteria (i.e. R-value, T value, mean squared error, and model simplicity were used to evaluate model’s performance. A well-trained optimum 4-6-2 ANN provided the best accuracy in modeling contact area and rolling resistance with regression coefficients of 0.998 and 0.999 and T value and MSE of 0.996 and 2.55 × 10−12, respectively. It was found that ANNs due to faster, more precise, and considerably reliable computation of multivariable, nonlinear, and complex computations are highly appropriate for soil–wheel interaction modeling.

Full Text Available The study used 69 brains (n = 69 from adult dog cadavers, divided by their skull type into three groups, brachi (B, dolicho (D and mesaticephalic (M (n = 23 each, and aimed: (1 to determine whether the Bronson equation may be applied, without reservation, to estimate brain weight (BW in brachy (B, dolicho (D, and mesaticephalic (M dog breeds; and (2 to evaluate which breeds are more closely related to each other in an evolutionary scenario. All subjects were identified by sex, age, breed, and body weight (bw. An oscillating saw was used for a circumferential craniotomy to open the skulls; the brains were removed and weighed using a digital scale. For statistical analysis, p-values < 0.05 were considered significant. The work demonstrated a strong relationship between the observed and predicted BW by using the Bronson equation. It was possible to hypothesize that groups B and D present a greater encephalization level than M breeds, that B and D dog breeds are more closely related to each other than to M, and from the three groups, the D individuals presented the highest brain mass mean.

Over the last decade, the National Wildlife Health Center of the United States Geological Survey has documented various largescale mortalities of birds caused by infectious and non-infectious disease agents. Some of these mortality events have unusual or unidentified etiologies and have been recurring. While some of the causes of mortalities have been elucidated, others remain in various stages of investigation and identification. Two examples are discussed: 1) Leyogonimus polyoon (Class: Trematoda), not found in the New World until 1999, causes severe enteritis and has killed over 15 000 American Coot Fulica americana in the upper mid-western United States. The geographic range of this parasite within North America is predicted to be limited to the Great Lakes Basin. 2) In the early 1990s, estimates of up to 6% of the North American population of the Eared Grebe Podiceps nigricollis died at Salton Sea, California, with smaller mortalities occurring throughout the 1990s. Birds were observed to have unusual preening behaviour, and to congregate at freshwater drains and move onto land. Suggested etiologies included interactions of contaminants, immuno-suppression, an unusual form of a bacterial disease, and an unknown biotoxin. During studies carried out from 2000 to 2003, Eared Grebe mortality did not approach the level seen in the early 1990s and, although bacteria were identified as minor factors, the principal cause of mortality remains undetermined. The potential population impact of these emerging and novel disease agents is currently unknown.

Solid-liquid separation of animal slurry, with solid fractions used for composting, has gained interest recently. However, efficient composting of separated animal slurry solid fractions (SSFs) requires a better understanding of the process dynamics in terms of important physical parameters and their interacting physical relationships in the composting matrix. Here we monitored moisture content, bulk density, particle density and air-filled porosity (AFP) during composting of SSF collected from four commercially available solid-liquid separators. Composting was performed in laboratory-scale reactors for 30 days (d) under forced aeration and measurements were conducted on the solid samples at the beginning of composting and at 10-d intervals during composting. The results suggest that differences in initial physical properties of SSF influence the development of compost maximum temperatures (40-70 degreeC). Depending on SSF, total wet mass and volume losses (expressed as % of initial value) were up to 37% and 34%, respectively. After 30 d of composting, relative losses of total solids varied from 17.9% to 21.7% and of volatile solids (VS) from 21.3% to 27.5%, depending on SSF. VS losses in all composts showed different dynamics as described by the first-order kinetic equation. The estimated component particle density of 1441 kg m-3 for VS and 2625 kg m-3 for fixed solids can be used to improve estimates of AFP for SSF within the range tested. The linear relationship between wet bulk density and AFP reported by previous researchers held true for SSF.

In this study, the author attempted to correlate clinical factors significant in cases of cervical myelopathy with postoperative recovery. It is hoped that the results will aid in the preoperative prediction of surgical outcomes. The factors considered were the transverse area of the spinal cord, the cord compression rate, the presence of a high intensity area in T2-weighted MRI, the duration of symptoms before surgery, and age at surgery. Because there are variations in the transverse area of the spinal cord, 100 normal individuals were selected and the standard transverse area was calculated. The transverse area of the spinal cord and the cord constriction rate in the myelopathy cases was then measured and compared to the standard. The data indicated that the constriction rate was most relevant to recovery rate. Clinical thresholds found to correlate with a better than average rate of recovery in cases of cervical spondylotic myelopathy (CSM) were: a cord constriction rate; under 28.7%, cord compression rate; over 0.38, duration of symptoms before surgery; less than 9.2 months, and age at surgery; under 59.2 yrs. In patients with ossification of the longitudinal ligament (OPLL), cord constriction rate; under 36.2%, cord compression rate; over 0.30, duration of symptoms before surgery; less than 14.2 months, and age at surgery; under 57.6 yrs., all correlated with superior recovery, as did cord constriction rate; under 22.3%, and duration of symptoms before surgery; less than 3.7 months with patients suffering from cervical disc herniation (CDH). Furthermore, the absence of a T2-weighted high intensity area in CSM and OPLL patients also correlated with improved recovery. These results suggest that a favorable postoperative recovery rate can be expected in cases of cervical myelopathy that conform to the above criteria. (author)

Malaria transmission intensity is modeled from the starting perspective of individual vector mosquitoes and is expressed directly as the entomologic inoculation rate (EIR). The potential of individual mosquitoes to transmit malaria during their lifetime is presented graphically as a function of their feeding cycle length and survival, human biting preferences, and the parasite sporogonic incubation period. The EIR is then calculated as the product of 1) the potential of individual vectors to transmit malaria during their lifetime, 2) vector emergence rate relative to human population size, and 3) the infectiousness of the human population to vectors. Thus, impacts on more than one of these parameters will amplify each other's effects. The EIRs transmitted by the dominant vector species at four malaria-endemic sites from Papua New Guinea, Tanzania, and Nigeria were predicted using field measurements of these characteristics together with human biting rate and human reservoir infectiousness. This model predicted EIRs (+/- SD) that are 1.13 +/- 0.37 (range = 0.84-1.59) times those measured in the field. For these four sites, mosquito emergence rate and lifetime transmission potential were more important determinants of the EIR than human reservoir infectiousness. This model and the input parameters from the four sites allow the potential impacts of various control measures on malaria transmission intensity to be tested under a range of endemic conditions. The model has potential applications for the development and implementation of transmission control measures and for public health education.

A 3-dimensional, discrete-feature hydrological model is developed. The model integrates structural and hydrologic data for the Aespoe site, on scales ranging from semi regional fracture zones to individual fractures in the vicinity of the nuclear waste canisters. Predictedparameters for the near field include fracture spacing, fracture aperture, and Darcy velocity at each of forty canister deposition holes. Parameters for the far field include discharge location, Darcy velocity, effective longitudinal dispersion coefficient and head gradient, flow porosity, and flow wetted surface, for each canister source that discharges to the biosphere. Results are presented in the form of statistical summaries for a total of 42 calculation cases, which treat a set of 25 model variants in various combinations. The variants for the SITE-94 Reference Case model address conceptual and parametric uncertainty related to the site-scale hydrogeologic model and its properties, the fracture network within the repository, effective semi regional boundary conditions for the model, and the disturbed-rock zone around the repository tunnels and shafts. Two calculation cases simulate hydrologic conditions that are predicted to occur during future glacial episodes. 30 refs

Geant4 is the leading detector simulation toolkit used in high energy physics to design detectors and to optimize calibration and reconstruction software. It employs a set of carefully validated physics models to simulate interactions of particles with matter across a wide range of interaction energies. These models, especially the hadronic ones, rely largely on directly measured cross-sections and phenomenological predictions with physically motivated parameters estimated by theoretical calculation or measurement. Because these models are tuned to cover a very wide range of possible simulation tasks, they may not always be optimized for a given process or a given material. This raises several critical questions, e.g. how sensitive Geant4 predictions are to the variations of the model parameters, or what uncertainties are associated with a particular tune of a Geant4 physics model, or a group of models, or how to consistently derive guidance for Geant4 model development and improvement from a wide range of available experimental data. We have designed and implemented a comprehensive, modular, user-friendly software toolkit to study and address such questions. It allows one to easily modify parameters of one or several Geant4 physics models involved in the simulation, and to perform collective analysis of multiple variants of the resulting physics observables of interest and comparison against a variety of corresponding experimental data. Based on modern event-processing infrastructure software, the toolkit offers a variety of attractive features, e.g. flexible run-time configurable workflow, comprehensive bookkeeping, easy to expand collection of analytical components. Design, implementation technology, and key functionalities of the toolkit are presented and illustrated with results obtained with Geant4 key hadronic models.

Full Text Available The present study focuses on friction welding process parameter optimization using a hybrid technique of ANN and different optimization algorithms. This optimization techniques are not only for the effective process modelling, but also to illustrate the correlation between the input and output responses of the friction welding of Incoloy 800H. In addition the focus is also to obtain optimal strength and hardness of joints with minimum burn off length. ANN based approaches could model this welding process of INCOLOY 800H in both forward and reverse directions efficiently, which are required for the automation of the same. Five different training algorithms were used to train ANN for both forward and reverse mapping and ANN tuned force approach was used for optimization. The paper makes a robust comparison of the performances of the five algorithms employing standard statistical indices. The results showed that GANN with 4-9-3 for forward and 4-7-3 for reverse mapping arrangement could outperform the other four approaches in most of the cases but not in all. Experiments on tensile strength (TS, microhardness (H and burn off length (BOL of the joints were performed with optimised parameter. It is concluded that this ANN model with genetic algorithm may provide good ability to predict the friction welding process parameters to weld Incoloy 800H.

The aim of this study was to evaluate if subjective symptoms, radiographic and especially MR parameters of cervical spine involvement, can predict neurologic dysfunction in patients with severe rheumatoid arthritis (RA). Sequential radiographs, MR imaging, and neurologic examination were performed yearly in 46 consecutive RA patients with symptoms indicative of cervical spine involvement. Radiographic parameters were erosions of the dens or intervertebral joints, disc-space narrowing, horizontal and vertical atlantoaxial subluxation, subluxations below C2, and the diameter of the spinal canal. The MR features evaluated were presence of dens and atlas erosion, brainstem compression, subarachnoid space encroachment, pannus around the dens, abnormal fat body caudal to the clivus, cervicomedullary angle, and distance of the dens to the line of McRae. Muscle weakness was associated with a tenfold increased risk of neurologic dysfunction. Radiographic parameters were not associated. On MR images atlas erosion and a decreased distance of the dens to the line of McRae showed a fivefold increased risk of neurologic dysfunction. Subarachnoid space encroachment was associated with a 12-fold increased risk. Rheumatoid arthritis patients with muscle weakness and subarachnoid space encroachment of the entire cervical spine have a highly increased risk of developing neurologic dysfunction. (orig.)

The aim of this study was to evaluate if subjective symptoms, radiographic and especially MR parameters of cervical spine involvement, can predict neurologic dysfunction in patients with severe rheumatoid arthritis (RA). Sequential radiographs, MR imaging, and neurologic examination were performed yearly in 46 consecutive RA patients with symptoms indicative of cervical spine involvement. Radiographic parameters were erosions of the dens or intervertebral joints, disc-space narrowing, horizontal and vertical atlantoaxial subluxation, subluxations below C2, and the diameter of the spinal canal. The MR features evaluated were presence of dens and atlas erosion, brainstem compression, subarachnoid space encroachment, pannus around the dens, abnormal fat body caudal to the clivus, cervicomedullary angle, and distance of the dens to the line of McRae. Muscle weakness was associated with a tenfold increased risk of neurologic dysfunction. Radiographic parameters were not associated. On MR images atlas erosion and a decreased distance of the dens to the line of McRae showed a fivefold increased risk of neurologic dysfunction. Subarachnoid space encroachment was associated with a 12-fold increased risk. Rheumatoid arthritis patients with muscle weakness and subarachnoid space encroachment of the entire cervical spine have a highly increased risk of developing neurologic dysfunction. (orig.)

To assess whether daily variations in three parameters recorded by non-invasive ventilation (NIV) software (respiratory rate (RR), percentage of respiratory cycles triggered by the patient (%Trigg) and NIV daily use) predict the risk of exacerbation in patients with chronic obstructive pulmonary disease (COPD) treated by home NIV. Patients completed the EXACT-Pro questionnaire daily to detect exacerbations. The 25th and 75th percentiles of each 24 h NIV parameter were calculated and updated daily. For a given day, when the value of any parameter was >75th or 75th, 'low value' <25th). Stratified conditional logistic regressions estimated the risk of exacerbation when ≥2 days (for RR and %Trigg) or ≥3 days (for NIV use) out of five had an 'abnormal value'. Sixty-four patients were included. Twenty-one exacerbations were detected and medically confirmed. The risk of exacerbation was increased when RR (OR 5.6, 95% CI 1.4 to 22.4) and %Trigg (OR 4.0, 95% CI 1.1 to 14.5) were considered as 'high value' on ≥2 days out of five. This proof-of-concept study shows that daily variations in RR and %Trigg are predictors of an exacerbation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Full Text Available This study details the artificial neural network (ANN modelling of a diesel engine to predict the torque, power, brake-specific fuel consumption and pollutant emissions, including carbon dioxide, carbon monoxide, nitrogen oxides, total hydrocarbons and filter smoke number. To collect data for training and testing the neural network, experiments were performed on a four cylinder, four stroke compression ignition engine. A total of 108 test points were run on a dynamometer. For the first part of this work, a parameter packet was used as the inputs for the neural network, and satisfactory regression was found with the outputs (over ~95%, excluding total hydrocarbons. The second stage of this work addressed developing new networks with additional inputs for predicting the total hydrocarbons, and the regression was raised from 75 % to 90 %. This study shows that the ANN approach can be used for accurately predicting characteristic values of an internal combustion engine and that the neural network performance can be increased using additional related input data.

Neoplasms of the thorax encompass those derived from the thoracic wall, trachea, mediastinum, lungs and pleura. They represent a wide variety of lesions including benign and malignant tumors arising from many tissues. The large surface area, 60 to 90 m{sup 2} in man, represented by the respiratory epithelium and associated thoracic structures are ideal targets for carcinogens carried by inspired air. The topic of discussion in this report is the epidemiology, etiology, and mechanisms of spontaneous intrathoracic neoplasia in animals and man. Much of what we know or suspect about thoracic neoplasia in animals has been extrapolated from experimentally-induced neoplasms.

To record and analyse bioelectrical activity of the uterine muscle in the course of physiological pregnancy, labour and threatening premature labour; to define which parameters from the analysis of both electrohysterogram and mechanical activity signal allow us to predict threatening premature labour. Material comprised 62 pregnant women: Group I--27 patients in their first physiological pregnancy, Group II--21 patients in their first pregnancy with symptoms of threatening premature labour, and Group III--14 patients in the first labour period. The on-line analysis of the mechanical (TOCO) and electrical (EHG) contraction activity relied on determination of quantitative parameters of detected uterine contractions. The obtained statistical results demonstrated a possibility to differentiate between Group I and II through the amplitude and contraction area for EHG signal, and only the contraction amplitude for TOCO signal. Additionally, significant differentiating parameters for electrohysterogram are: contraction power and its median frequency. Analyzing Group I and III, significant differences were noted for contraction amplitude and area obtained both from EHG and TOCO signals. Similarly, the contraction power (from EHG) enables us to assign the contractions either to records from Group I or to labour type. There was no significant difference noted between Group II and III. Identification of pregnant women at risk of premature labour should lead to their inclusion in rigorous perinatal surveillance. This requires novel, more sensitive methods that are able to detect early symptoms of the uterine contraction activity increase. Electrohysterography provides complete information on principles of bioelectrical uterine activity. Quantitative parameters of EHG analysis enable the detection of records (contractions) with the symptoms of premature uterine contraction activity.

Uncertainty derived from one of the process models – such as one-dimensional secondary settling tank (SST) models – can impact the output of the other process models, e.g., biokinetic (ASM1), as well as the integrated wastewater treatment plant (WWTP) models. The model structure and parameter...... and from the last aerobic bioreactor upstream to the SST (Garrett/hydraulic method). For model structure uncertainty, two one-dimensional secondary settling tank (1-D SST) models are assessed, including a first-order model (the widely used Takács-model), in which the feasibility of using measured...... uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based...

Full Text Available The Particle Swarm Optimization (PSO and Support Vector Machines (SVMs approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R2 higher than 0.8.

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this study, we use the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts.

Soft computing techniques are recently becoming very popular in the oil industry. A number of computational intelligence-based predictive methods have been widely applied in the industry with high prediction capabilities. Some of the popular methods include feed-forward neural networks, radial basis function network, generalized regression neural network, functional networks, support vector regression and adaptive network fuzzy inference system. A comparative study among most popular soft computing techniques is presented using a large dataset published in literature describing multimodal pore systems in the Arab D formation. The inputs to the models are air porosity, grain density, and Thomeer parameters obtained using mercury injection capillary pressure profiles. Corrected air permeability is the target variable. Applying developed permeability models in recent reservoir characterization workflow ensures consistency between micro and macro scale information represented mainly by Thomeer parameters and absolute permeability. The dataset was divided into two parts with 80% of data used for training and 20% for testing. The target permeability variable was transformed to the logarithmic scale as a pre-processing step and to show better correlations with the input variables. Statistical and graphical analysis of the results including permeability cross-plots and detailed error measures were created. In general, the comparative study showed very close results among the developed models. The feed-forward neural network permeability model showed the lowest average relative error, average absolute relative error, standard deviations of error and root means squares making it the best model for such problems. Adaptive network fuzzy inference system also showed very good results.

Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

Full Text Available The Grade-H high strength steel is used in the manufacturing of many civilian and military products. The procedures of manufacturing these parts have several turning operations. The key factors for the manufacturing of these parts are the accuracy, surface roughness (Ra, and material removal rate (MRR. The production line of these parts contains many CNC turning machines to get good accuracy and repeatability. The manufacturing engineer should fulfill the required surface roughness value according to the design drawing from first trail (otherwise these parts will be rejected as well as keeping his eye on maximum metal removal rate. The rejection of these parts at any processing stage will represent huge problems to any factory because the processing and raw material of these parts are very expensive. In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. These parameters were investigated to get the minimum surface roughness. In addition, a mathematical model for surface roughness was obtained from the experimental data using a regression analysis method. The experimental data are then compared with both the regression analysis results and ANFIS (Adaptive Network-based Fuzzy Inference System estimations.

In this study, Wood Ash (WA) prepared from the uncontrolled burning of the saw dust is evaluated for its suitability as partial cement replacement in conventional concrete. The saw dust has been acquired from a wood polishing unit. The physical, chemical and mineralogical characteristics of WA is presented and analyzed. The strength parameters (compressive strength, split tensile strength and flexural strength) of concrete with blended WA cement are evaluated and studied. Two different water-to-binder ratio (0.4 and 0.45) and five different replacement percentages of WA (5%, 10%, 15%, 18% and 20%) including control specimens for both water-to-cement ratio is considered. Results of compressive strength, split tensile strength and flexural strength showed that the strength properties of concrete mixture decreased marginally with increase in wood ash contents, but strength increased with later age. The XRD test results and chemical analysis of WA showed that it contains amorphous silica and thus can be used as cement replacing material. Through the analysis of results obtained in this study, it was concluded that WA could be blended with cement without adversely affecting the strength properties of concrete. Also using a new statistical theory of the Support Vector Machine (SVM), strength parameters were predicted by developing a suitable model and as a result, the application of soft computing in structural engineering has been successfully presented in this research paper.

Full Text Available In this study, Wood Ash (WA prepared from the uncontrolled burning of the saw dust is evaluated for its suitability as partial cement replacement in conventional concrete. The saw dust has been acquired from a wood polishing unit. The physical, chemical and mineralogical characteristics of WA is presented and analyzed. The strength parameters (compressive strength, split tensile strength and flexural strength of concrete with blended WA cement are evaluated and studied. Two different water-to-binder ratio (0.4 and 0.45 and five different replacement percentages of WA (5%, 10%, 15%, 18% and 20% including control specimens for both water-to-cement ratio is considered. Results of compressive strength, split tensile strength and flexural strength showed that the strength properties of concrete mixture decreased marginally with increase in wood ash contents, but strength increased with later age. The XRD test results and chemical analysis of WA showed that it contains amorphous silica and thus can be used as cement replacing material. Through the analysis of results obtained in this study, it was concluded that WA could be blended with cement without adversely affecting the strength properties of concrete. Also using a new statistical theory of the Support Vector Machine (SVM, strength parameters were predicted by developing a suitable model and as a result, the application of soft computing in structural engineering has been successfully presented in this research paper.

Residual environmental sound can mask intrusive4 (unwanted) sound. It is a factor that can affect noise impacts and must be considered both in noise-impact studies and in noise-mitigation designs. Models for quantitative prediction of sensation level (audibility) and psychological effects of intrusive noise require an input with 1/3 octave-band spectral resolution of environmental masking noise. However, the majority of published residual environmental masking-noise data are given with either octave-band frequency resolution or only single A-weighted decibel values. A model has been developed that enables estimation of 1/3 octave-band residual environmental masking-noise spectra and relates certain environmental parameters to A-weighted sound level. This model provides a correlation among three environmental conditions: measured residual A-weighted sound-pressure level, proximity to a major roadway, and population density. Cited field-study data were used to compute the most probable 1/3 octave-band sound-pressure spectrum corresponding to any selected one of these three inputs. In turn, such spectra can be used as an input to models for prediction of noise impacts. This paper discusses specific algorithms included in the newly developed computer program ENMASK. In addition, the relative audibility of the environmental masking-noise spectra at different A-weighted sound levels is discussed, which is determined by using the methodology of program ENAUDIBL.

We have identified 61 relativistic electron enhancement events and 21 relativistic electron persistent depletion events during 1996 to 2006 from the Geostationary Operational Environmental Satellite (GOES) 8 and 10 using data from the Energetic Particle Sensor (EPS) >2 MeV fluxes. We then performed a superposed epoch time analysis of the events to find the characteristic solar wind parameters that determine the occurrence of such events, using the OMNI database. We found that there are clear differences between the enhancement events and the persistent depletion events, and we used these to establish a set of threshold values in solar wind speed, proton density and interplanetary magnetic field (IMF) Bz that can potentially be useful to predict sudden increases in flux. Persistent depletion events are characterized by a low solar wind speed, a sudden increase in proton density that remains elevated for a few days, and a northward turning of IMF Bz shortly after the depletion starts. We have also found that all relativistic electron enhancement or persistent depletion events occur when some geomagnetic disturbance is present, either a coronal mass ejection or a corotational interaction region; however, the storm index, SYM-H, does not show a strong connection with relativistic electron enhancement events or persistent depletion events. We have tested a simple threshold method for predictability of relativistic electron enhancement events using data from GOES 11 for the years 2007-2010 and found that around 90% of large increases in electron fluxes can be identified with this method.

Iron is the most abundant transition metal in space. Its abundance is similar to that of magnesium, and until today only, FeO and FeCN have been detected. However, magnesium-bearing compounds such as MgCN, MgNC, and HMgNC are found in IRC+10216. It seems that the hydrides of iron cyanide/isocyanide could be good candidates to be present in space. In the present work we carried out a characterization of the different minima on the quintet and triplet [C, Fe, H, N] potential energy surfaces, employing several theoretical approaches. The most stable isomers are predicted to be hydride of iron cyanide HFeCN, and isocyanide HFeNC, in their {sup 5}Δ states. Both isomers are found to be quasi-isoenergetics. The HFeNC isomer is predicted to lie about 0.5 kcal/mol below HFeCN. The barrier for the interconversion process is estimated to be around 6.0 kcal/mol, making this process unfeasible under low temperature conditions, such as those in the interstellar medium. Therefore, both HFeCN and HFeNC could be candidates for their detection. We report geometrical parameters, vibrational frequencies, and rotational constants that could help with their experimental characterization.

Iron is the most abundant transition metal in space. Its abundance is similar to that of magnesium, and until today only, FeO and FeCN have been detected. However, magnesium-bearing compounds such as MgCN, MgNC, and HMgNC are found in IRC+10216. It seems that the hydrides of iron cyanide/isocyanide could be good candidates to be present in space. In the present work we carried out a characterization of the different minima on the quintet and triplet [C, Fe, H, N] potential energy surfaces, employing several theoretical approaches. The most stable isomers are predicted to be hydride of iron cyanide HFeCN, and isocyanide HFeNC, in their 5 Δ states. Both isomers are found to be quasi-isoenergetics. The HFeNC isomer is predicted to lie about 0.5 kcal/mol below HFeCN. The barrier for the interconversion process is estimated to be around 6.0 kcal/mol, making this process unfeasible under low temperature conditions, such as those in the interstellar medium. Therefore, both HFeCN and HFeNC could be candidates for their detection. We report geometrical parameters, vibrational frequencies, and rotational constants that could help with their experimental characterization.

The use of neoadjuvant chemotherapy followed by tumor reduction surgery, also called interval debulking surgery (IDS), is considered an alternative therapeutic regimen for selected patients with advanced stage epithelial ovarian cancer (EOC). Although minimal residual disease has been proven to be a prognostic factor in traditional cytoreduction for advanced stage EOC, predictive factors after IDS still remain unexplored. The aim of this study was to determine the prognostic value of post-neoadjuvant histologic changes with clinical outcome. Three pathologists evaluated 67 cases for the following parameters: fibrosis, necrosis, residual tumor, and inflammation. The Cohen's kappa statistic was used to measure agreement among pathologists. Univariate and multivariate Cox proportional hazards models were used to determine the association between histologic parameters and recurrence-free survival (RFS) and overall survival (OS). There was substantial to almost perfect agreement among the three pathologists in all four histologic parameters (k ranged from 0.65 to 0.97). Fibrosis was associated with longer RFS (P = 0.0257) with a median of 20 months for tumors with fibrosis (3+) versus 12 months for tumors with fibrosis (1+, 2+) and longer OS (P = 0.0249) with a median of 51 months for tumors with fibrosis (3+) versus 32 months for tumors with fibrosis (1+, 2+). Our results revealed that patients with tumors exhibiting fibrosis (1+, 2+), as well as necrosis (0, 1+), had significant shorter RFS and OS (P = 0.059 and P = 0.0234, respectively). We suggest that the assessment of fibrosis and necrosis should be implemented in pathologic evaluation and prospectively validated in future studies.

Full Text Available To identify non-invasive clinical parameters to predict urodynamic bladder outlet obstruction (BOO in patients with benign prostatic hyperplasia (BPH using causal Bayesian networks (CBN.From October 2004 to August 2013, 1,381 eligible BPH patients with complete data were selected for analysis. The following clinical variables were considered: age, total prostate volume (TPV, transition zone volume (TZV, prostate specific antigen (PSA, maximum flow rate (Qmax, and post-void residual volume (PVR on uroflowmetry, and International Prostate Symptom Score (IPSS. Among these variables, the independent predictors of BOO were selected using the CBN model. The predictive performance of the CBN model using the selected variables was verified through a logistic regression (LR model with the same dataset.Mean age, TPV, and IPSS were 6.2 (±7.3, SD years, 48.5 (±25.9 ml, and 17.9 (±7.9, respectively. The mean BOO index was 35.1 (±25.2 and 477 patients (34.5% had urodynamic BOO (BOO index ≥40. By using the CBN model, we identified TPV, Qmax, and PVR as independent predictors of BOO. With these three variables, the BOO prediction accuracy was 73.5%. The LR model showed a similar accuracy (77.0%. However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020.Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

Until recently, the cause of dry eye syndrome was uncertain and the treatment was palliative. Since discovering that dry eyes are caused by inflammation, there has been an abundance of research focusing on anti-inflammatory therapies, other contributing causes, and better diagnostic testing. This review summarizes some of the interesting published research on ocular surface disease over the past year. The definition of dry eye now highlights the omnipresent symptom of blurry vision. The re-evaluation of ocular surface staining, tear meniscus height, and visual change will allow for a better diagnosis and understanding of dry eyes. Punctal plugs, and oral and topical anti-inflammatory use will strengthen our arsenal against ocular surface disease. Major progress has occurred in the past few years in gaining a better understanding of the etiology of dry eye syndrome, which will inevitably lead to more effective therapeutic options.

Full Text Available Objective: Psoas abscess (PA is a rare infection disease, which is difficult to diagnose. In the present study, we aimed to evaluate etiological factors and treatment results of patients with PA. Methods: Files of 20 patients who were diagnosed as PA between December 2006 and January 2013, were retrospectively analyzed. Patient’s whose data were entirely reached and diagnosed by Ultrasonography and/or Computed Tomography as an exact PA were included to the study. Results: The mean age of the 20 patients was 48.8 (range 17-82 year, and 6 of them were female and remaining were male. Psoas abscess were on the right side in 12 patients (60%, on the left side in seven patients (35%, and bilateral in one (5%. According to data records four patients had Diabetes Mellitus (20%, two had Hypertension (10%, one had cerebrovascular disease (5%, one had tuberculosis (5%, one had hyperthyroidism (5%, one had mental retardation (5%, and one had paraplegia (5%. Six case (30% were diagnosed as a primary psoas abscess (pPA, sPA and remaining (n=14, %70 were diagnosed as secondary. Percutaneous drainage was performed to 13 patients (65% and exploration was performed to three patients (15% as a treatment modality. Remaining four patients (20% were followed by medical treatment. Conclusion: Psoas abscess is rare and have variable and non-specific clinical characteristic, which may lead to difficulty in diagnosis. In developed and developing countries, it has been reported that the most common causes of sPA are Pott's disease, and Crohn's disease, also it should be taken into account that open surgery and urinary tract stone disease can receive a significant portion of the etiological factors. J Clin Exp Invest 2014; 5 (1: 59-63

The etiologic profile and possible predictors of etiology in children with spastic quadriplegia were assessed in a consecutive cohort of children with this motor impairment. Medical records from a single pediatric neurology practice over a 14-year interval were retrospectively and systematically reviewed. Variables comprised possible demographic, prenatal, perinatal, and postnatal risk factors. Of the 99 patients included in the study, 39 were premature (quadriplegia was 83%, with differing underlying etiologies depending on gestational age. These results should help guide physicians in investigating possible underlying etiologies in patients with spastic quadriplegia.

Multiple new procedures for treatment of complex anal fistula have been described in the past decades, but an ideal single technique has yet not been identified. Factors that predict the outcome are required to identify the best procedure for each individual patient. The aim of this study was to find those predictors for advancement flap at midterm follow-up. From 2012 to 2015 in a tertiary university clinic, all patients who underwent advancement flap for treatment of complex cryptoglandular fistula were prospectively enrolled. Pre- and postoperatively standardized anamnestic and clinical examinations were performed. Predictive factors for therapy failure were identified using univariate and multivariate analysis. Out of 65 patients, 61 (93%) completed all examinations and were included in the study. Therapy failure after a mean follow-up period of 25 months occurred in total n = 11 patients (18%). There was no significant disturbance of continence among the entire study cohort as shown by the incontinence score (preop 0.34 ± 0.91 pts., postop 0.37 ± 0.97 pts.; p = 0.59). Univariate analysis for risk factors for therapy failure revealed age (p = 0.004), history of surgical abscess drainage (p = 0.04), BMI (p = 0.002), suprasphincteric fistula (p = 0.019) and horseshoe abscess (p = 0.036) as independent parameters for therapy failure. During multivariate analysis, only history of surgical abscess drainage (OR = 8.09, p = 0.048, 95% CI 0.98-64.96), suprasphincteric fistula (OR = 6.83, p = 0.032, 95% CI 1.17-6.83) and BMI (OR = 1.23, p = 0.017, 95% CI 1.03-1.46) were independent parameters for therapy failure. Advancement flap for treatment of complex fistula is effective and has low risk of disturbed continence. BMI, suprasphincteric fistula and history of surgical abscess drainage are predictors for therapy failure.

Full Text Available Abstract Background We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC, that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024–1029. We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590 of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6°C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, ε of 0.21 and an RMSE of 45.1°C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3°C, R2 of 0.47 for the same data and has similar performance to a Random Forest model (RMSE of 44.5°C, R2 of 0.55. However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors.

We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024-1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581-590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6 degrees C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, epsilon of 0.21) and an RMSE of 45.1 degrees C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3 degrees C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5 degrees C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors.

Full Text Available Aim: The aim of the present study was the evaluation of non-microbial salivary caries activity parameters and salivary biochemical indicators in predicting dental caries. Materials and Methods: The present study was carried out on 60 children, aged 4-6 years, selected from the schools of Panchkula district, Haryana, on the basis of their caries status. Level of hydration, flow rate, pH, buffering capacity, relative viscosity, calcium, phosphorus and alkaline phosphatase levels in caries-free and caries-active children were evaluated. Results: Results showed that 90% of subjects in the caries-free group and 30% of subjects in the caries-active group had normal level of hydration value of less than 60 s and the difference was found to be statistically very highly significant. Normal flow rate of stimulated saliva was found in 90% of the subjects in caries-free group and 33.3% subjects in the caries active group and difference was found to be statistically very highly significant. Adequate salivary pH was found in 100% subjects in caries-free group and 30% in caries-active group and the difference was statistically very highly significant. Conclusion: To conclude, within limitations of this study, it became clear that normal level of hydration and higher values for flow rate, pH, buffering capacity of saliva lead to good oral health and a reduced caries occurrence. Increased salivary viscosity plays a role in increasing caries incidence. Salivary biochemical indicators like calcium, phosphorus and alkaline phosphatase also play their respective role in determining caries susceptibility of an individual. These salivary parameters can be used as diagnostic tool for caries risk assessment.

Discussions about withdrawal of life-sustaining therapies often include family members of critically ill patients. These conversations should address essential components of the dying process, including expected time to death after withdrawal. The study objective was to aid physician communication about the dying process by identifying predictors of time to death after terminal withdrawal of mechanical ventilation. We conducted an observational analysis from a single-center, before-after evaluation of an intervention to improve palliative care. We studied 330 patients who died after terminal withdrawal of mechanical ventilation. Predictors included patient demographics, laboratory, respiratory, and physiologic variables, and medication use. The median time to death for the entire cohort was 0.58 hours (interquartile range (IQR) 0.22-2.25 hours) after withdrawal of mechanical ventilation. Using Cox regression, independent predictors of shorter time to death included higher positive end-expiratory pressure (per 1 cm H2O hazard ratio [HR], 1.07; 95% CI 1.04-1.11); higher static pressure (per 1 cm H2O HR, 1.03; 95% CI 1.01-1.04); extubation prior to death (HR, 1.41; 95% CI 1.06-1.86); and presence of diabetes (HR, 1.75; 95% CI 1.25-2.44). Higher noninvasive mean arterial pressure predicted longer time to death (per 1 mmHg HR, 0.98; 95% CI 0.97-0.99). Comorbid illness and key respiratory and physiologic parameters may inform physician predictions of time to death after withdrawal of mechanical ventilation. An understanding of the predictors of time to death may facilitate discussions with family members of dying patients and improve communication about end-of-life care.

The recent experimental programme conducted in the PROTEUS research reactor at the Paul Scherrer Institute (PSI) has concerned detailed investigations of advanced light water reactor (LWR) fuels. More than fifteen different configurations of the multi-zone critical facility have been studied, each of them requiring accurate estimation of operational safety parameters, in particular the critical driver loadings, shutdown rod worths and the effective delayed neutron fraction {beta}{sub eff}. The current paper presents a full-scale 3D Monte Carlo model for the facility, set up using the MCNPX code, which has been employed for calculation of the operational characteristics for seven different LWR-PROTEUS configurations. Thereby, a variety of nuclear data libraries (viz. ENDF/B6v2, ENDF/B6v8, JEF2.2, JEFF3.0, JEFF3.1, JENDL3.2, and JENDL3.3) have been used, and predictions of k{sub eff} and shutdown rod worths compared with experimental values. Even though certain library-specific trends have been observed, the k{sub eff} predictions are generally very satisfactory, viz. with discrepancies of <0.5% between calculation (C) and experiment (E). The results also confirm the consistent determination of reactivity variations, the C/E values for the shutdown (safety) rod worths being always within 5% of unity. In addition, the MCNP modelling of the multi-zone reactor has yielded interesting results for the delayed neutron fraction ({beta}{sub eff}) in the different configurations, a breakdown being made possible in each case in terms of delayed neutron group, fissioning nuclide, and reactor region.

Full Text Available The understanding of erectile physiology has improved the prompt diagnosis and treatment of priapism. Priapism is defined as prolonged and persistent erection of the penis without sexual stimulation and failure to subside despite orgasm. Numerous etiologies of this condition are considered. Among others a disturbed detumescence mechanism, which may due to excess release of contractile neurotransmitters, obstruction of draining venules, malfunction of the intrinsic detumescence mechanism or prolonged relaxation of intracavernosal smooth muscle are postulated. Treatment of priapism varies from a conservative medical to a drastic surgical approach. Two main types of priapism; veno-occlusive low flow (ischemic and arterial high flow (non-ischemic, must be distinguished to choose the correct treatment option for each type. Patient history, physical examination, penile hemodynamics and corporeal metabolic blood quality provides distinction between a static or dynamic pathology. Priapism can be treated effectively with intracavernous vasoconstrictive agents or surgical shunting. Alternative options, such as intracavernous injection of methylene blue (MB or selective penile arterial embolization (SPEA, for the management of high and low flow priapism are described and a survey on current treatment modalities is given.

Uveitis remains an important cause of visual impairment, particularly in young patients. Idiopathic forms of intraocular inflammation should no longer be regarded as a presumed clinical entity, and the ophthalmologist must reconsider the specific etiology of primary uveitis when the clinical examination does not yield a definitive diagnosis or when the course of the disease on corticosteroids remains atypical. Laboratory tests based on serum analysis have limited value and should not be considered as diagnostic proof in different clinical presentations. The diagnostic management of infectious uveitis has been greatly improved by the use of molecular techniques applied to ocular fluids and tissues. Polymerase chain reaction (PCR) technology is a powerful tool that should be proposed in atypical cases of uveitis or retinitis of unclear but potentially infectious origin. This strategy is a major step before using unconventional and new immunomodulatory agents such as anti-TNF-alpha molecules. Under strict experimental conditions including adequate testing to rule out a possible contamination, PCR and its variants have changed our practical approach to intraocular inflammatory disorders and have provided new details for the understanding of infectious uveitis. The concept of pathogen-induced intraocular inflammation can be revisited in the light of molecular data obtained after anterior chamber paracentesis or diagnostic vitrectomy.

Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters, especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless, observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In this work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters is made, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, a Bayesian data assimilation method is chosen (because of available prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prior information and the result with the posterior information provided by the Bayesian data assimilation method. The result of the

Social aggression is a form of antisocial behavior in which social relationships and social status are used to damage reputations and inflict emotional harm on others. Despite extensive research examining the prevalence and consequences of social aggression, only a few studies have examined its genetic-environmental etiology, with markedly inconsistent results. We estimated the etiology of social aggression using the nuclear twin family (NTF) model. Maternal-report, paternal-report, and teacher-report data were collected for twin social aggression (N = 1030 pairs). We also examined the data using the classical twin (CT) model to evaluate whether its strict assumptions may have biased previous heritability estimates. The best-fitting NTF model for all informants was the ASFE model, indicating that additive genetic, sibling environmental, familial environmental, and non-shared environmental influences significantly contribute to the etiology of social aggression in middle childhood. However, the best-fitting CT model varied across informants, ranging from AE and ACE to CE. Specific heritability estimates for both NTF and CT models also varied across informants such that teacher reports indicated greater genetic influences and father reports indicated greater shared environmental influences. Although the specific NTF parameter estimates varied across informants, social aggression generally emerged as largely additive genetic (A = 0.15-0.77) and sibling environmental (S = 0.42-0.72) in origin. Such findings not only highlight an important role for individual genetic risk in the etiology of social aggression, but also raise important questions regarding the role of the environment.

Part 2 of this paper presents the experimental and analytical procedures used in the estimation of injection parameters from monitored vibration. The mechanical and flow‐induced sources of vibration in a fuel injector are detailed and the features of the resulting vibration response of the injector body are discussed. Experimental engine test and data acquisition procedures are described, and the use of an out‐of‐the‐engine test facility to confirm injection dependent vibration response is ou...

Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Full Text Available Introduction: Safe driving requires the ability of the driver to receive the messages and complying with them. The most significant consequences of noise pollution are on the human auditory system. Disorders in the auditory system can have harmful side effects for human health. By reducing this kind of pollution in large cities, the quality of life, which is one of the biggest goals of the governments, can be considerably increased. Hence, in the present research, some parameters of vehicle tires were examined as a source of noise pollution, and the results can be taken into consideration in noise pollution reduction. Material and Method: Several vehicles with different tire width were selected for measuring sound level. The sound levels were measured for moving vehicles with the use of the Statistical Pass By Method (SPB, ISO 11819-1. Following sound level measurements for moving vehicles and by considering tire width, mathematical model of noise level was predicted on the basis of the obtained information and by usage of SPSS program and considering vehicle tire parameters. Result: The result of this study showed that the vehicle speed and tire width can affect different sound levels emitted by moving tire on road surface. The average speed of vehicles can play an important role in the noise pollution. By increasing speed, rotation of the the tires on the asphalt is increased, as it is a known factors for noise pollution. Moreover, changing the speed of vehicles is accompanied with abnormal sounds of vehicle engine. According to regression model analysis, the obtained value of R2 for the model is 0.8367 which represents the coefficient of determination. Conclusion: The results suggest the main role of the vehicle speed and tire width in increasing the noise reaches to the drivers and consequent noise pollution, which demonstrates the necessity for noise control measures. According to the obtained model, it is understood that changes in noise

Full Text Available A 52-week decomposition study employing the soil larger fauna exclusion technique through litter bags of two mesh sizes (20 and 0.135 mm was conducted in a long-term (18 yr field experiment. Organic residues of contrasting quality of N, lignin (L, polyphenols (PP and cellulose (CL all in grams per kilogram: rice straw (RS: 4.5N, 22.2L, 3.9PP, 449CL, groundnut stover (GN: 21.2N, 71.4L, 8.1PP, 361CL, dipterocarp leaf litter (DP: 5.1N, 303L, 68.9PP, 271CL and tamarind leaf litter (TM: 11.6N, 190L, 27.7PP, 212CL were applied to soil annually to assess and predict soil larger fauna effects (LFE on decomposition based on the initial contents of the residue chemical constituents. Mass losses in all residues were not different under soil fauna inclusion and exclusion treatments during the early stage (up to week 4 after residue incorporation but became significantly higher under the inclusion than the exclusion treatments during the later stage (week 8 onwards. LFE were highest (2–51% under the resistant DP at most decomposition stages. During the early stage (weeks 1–4, both the initial contents of labile (N and CL and recalcitrant C, and recalcitrant C interaction with labile constituents of residues showed significant correlations (r = 0.64–0.90 with LFE. In the middle stage (week 16, LFE under resistant DP and TM had significant positive correlations with L, L + PP and L/CL. They were also affected by these quality parameters as shown by the multiple regression analysis. In the later stages (weeks 26–52, the L/CL ratio was the most prominent quality parameter affecting LFE. Keywords: Mesofauna and macrofauna, Microorganisms, Recalcitrant and labile compounds, Residue chemical composition, Tropical sandy soil

In 1769, William Cullen introduced the word "urticaria" (transient edematous papules, plaque with itching). Urticaria affects 15-25% of people at least once in their life time. It is a clinical reaction pattern triggered by many factors causing the liberation of vasoactive substances such as histamine, prostaglandins and kinins. Urticaria is classified according to its duration into acute (6 weeks duration). Various clinical investigations may be initiated to diagnosis the cause. To evaluate the types of chronic urticaria with reference to etiology from history and investigations. A total of 150 patients with chronic urticaria of more than six weeks were studied. Autologous serum skin test (ASST) was performed after physical urticarias were excluded. Standard batteries of tests were performed after ASST in all patients; and other specific investigations were done where necessary. Skin prick test was done in idiopathic urticaria. The study sample consisted of 62 male and 88 female patients with a mean age of 21-40 years. About 50% of patients showed an ASST positive reaction, 3.9% were positive for antinuclear antibody (ANA), IgE titer was elevated in 37%, H. pylori antibodies was positive in 26.7%. Thyroid antibodies were positive in 6.2%. Giardia and entamoeba histolytica was reported in 3.3% on routine stool examination and on urinalysis 8% had elevated WBC counts; 12% showed para nasal sinusitis, with maxillary sinusitis of 7.3%. Random blood sugar was high in 5.3%. Four patients had ASOM, two had positive KOH mount for dermatophytes, abdominal USG showed cholecystitis in two patients. Recurrent tonsillitis was noted in two patients. Urticaria following intake of NSAIDs was observed in four patients and with oral contraceptive pills in one patient. Contact urticaria to condom (latex) was seen in one patient. Cholinergic (4.7%) and dermographic (4.7%) urticaria were the predominant physical urticarias. Prick test was performed in idiopathic urticaria with maximum

The concept of etiology is analyzed and the possibilities and limitations of deterministic, probabilistic, and fuzzy etiology are explored. Different kinds of formal structures for the relation of causation are introduced which enable us to explicate the notion of cause on qualitative, comparative, and quantitative levels. The conceptual framework developed is an approach to a theory of causality that may be useful in etiologic research, in building nosological systems, and in differential diagnosis, therapeutic decision-making, and controlled clinical trials. The bearings of the theory are exemplified by examining the current Chlamydia pneumoniae hypothesis on the incidence of myocardial infarction.

Miscarriage is a frequent complication of human pregnancy: ∼50% to 70% of spontaneous conceptions are lost prior to the second trimester. Etiology of miscarriage includes genetic abnormalities, infections, immunological and implantation disorders, uterine and endocrine abnormalities, and lifestyle factors. Given such variability, knowledge regarding causes, pathophysiological mechanisms, and morphologies of primary early pregnancy loss has significant gaps; often, pregnancy losses remain unexplained. Pathologic evaluation of miscarriage tissue is an untapped source of knowledge. Although miscarriage specimens comprise a significant part of pathologists' workload, information reported from these specimens is typically of minimal clinical utility for delineating etiology or predicting recurrence risk. Standardized terminology is available, though not universally used. We reintroduce the terminology and review new information about early pregnancy losses and their morphologies. Current clinical terminology is inconsistent, hampering research progress. This review is a resource for diagnostic pathologists studying this complex problem.

Full Text Available Background: In 1769, William Cullen introduced the word "urticaria" (transient edematous papules, plaque with itching. Urticaria affects 15-25% of people at least once in their life time. It is a clinical reaction pattern triggered by many factors causing the liberation of vasoactive substances such as histamine, prostaglandins and kinins. Urticaria is classified according to its duration into acute (< 6 weeks duration and chronic (>6 weeks duration. Various clinical investigations may be initiated to diagnosis the cause. Aims: To evaluate the types of chronic urticaria with reference to etiology from history and investigations . Materials and Methods: A total of 150 patients with chronic urticaria of more than six weeks were studied. Autologous serum skin test (ASST was performed after physical urticarias were excluded. Standard batteries of tests were performed after ASST in all patients; and other specific investigations were done where necessary. Skin prick test was done in idiopathic urticaria. Results: The study sample consisted of 62 male and 88 female patients with a mean age of 21-40 years. About 50% of patients showed an ASST positive reaction, 3.9% were positive for antinuclear antibody (ANA, IgE titer was elevated in 37%, H. pylori antibodies was positive in 26.7%. Thyroid antibodies were positive in 6.2%. Giardia and entamoeba histolytica was reported in 3.3% on routine stool examination and on urinalysis 8% had elevated WBC counts; 12% showed para nasal sinusitis, with maxillary sinusitis of 7.3%. Random blood sugar was high in 5.3%. Four patients had ASOM, two had positive KOH mount for dermatophytes, abdominal USG showed cholecystitis in two patients. Recurrent tonsillitis was noted in two patients. Urticaria following intake of NSAIDs was observed in four patients and with oral contraceptive pills in one patient. Contact urticaria to condom (latex was seen in one patient. Cholinergic (4.7% and dermographic (4.7% urticaria were

Full Text Available The etiology and treatment of developmental stammering in childhood (DS, also called idiopathic stammering or stuttering are reviewed by a speech pathologist and psychologist at the University of Reading, UK.

Fournier's gangrene is a necrotizing infection affecting the male genitalia and perineum, caused by synergistic aerobic and anaerobic organisms. We report on a previously undescribed upper urinary tract etiology for this life-threatening infection.

Full Text Available The prognostic value of seizure etiology, neurologic examination, EEG, and neuroimaging in the neurodevelopmental outcome of 89 term infants with neonatal seizures was determined at the Children’s Hospital and Harvard Medical School, Boston, MA.

Status epilepticus is treated as a neurologic emergency and only later are the potential etiologies assessed. While sometimes the cause for status epilepticus is apparent (e.g., antiepileptic drug withdrawal), all too often it is not identified, even after extensive diagnostic testing has been performed. With emphasis on the less-common etiologies, this review will cover various probable and known causes of status epilepticus among adults, children, and those patients with refractory epilepsy. PMID:20231917

The purpose of this study was to investigate whether dynamic contrast enhanced MRI (DCE-MRI) data, both pharmacokinetic and empirical, can predict, prior to neoadjuvant chemotherapy, which patients are likely to have a shorter disease free survival (DFS) and overall survival (OS) interval following surgery. Traditional prognostic parameters were also included in the survival analysis. Consequently, a comparison of the prognostic value could be made between all the parameters studied. MR examinations were conducted on a 1.5 T system in 68 patients prior to the initiation of neoadjuvant chemotherapy. DCE-MRI consisted of a fast spoiled gradient echo sequence acquired over 35 phases with a mean temporal resolution of 11.3 s. Both pharmacokinetic and empirical parameters were derived from the DCE-MRI data. Kaplan-Meier survival plots were generated for each parameter and group comparisons were made utilising logrank tests. The results from the 54 patients entered into the univariate survival analysis demonstrated that traditional prognostic parameters (tumour grade, hormonal status and size), empirical parameters (maximum enhancement index, enhancement index at 30 s, area under the curve and initial slope) and adjuvant therapies demonstrated significant differences in survival intervals. Further multivariate Cox regression survival analysis revealed that empirical enhancement parameters contributed the greatest prediction of both DFS and OS in the resulting models. In conclusion, this study has demonstrated that in patients who exhibit high levels of perfusion and vessel permeability pre-treatment, evidenced by elevated empirical DCE-MRI parameters, a significantly lower disease free survival and overall survival can be expected.

The purpose of this study was to investigate whether dynamic contrast enhanced MRI (DCE-MRI) data, both pharmacokinetic and empirical, can predict, prior to neoadjuvant chemotherapy, which patients are likely to have a shorter disease free survival (DFS) and overall survival (OS) interval following surgery. Traditional prognostic parameters were also included in the survival analysis. Consequently, a comparison of the prognostic value could be made between all the parameters studied. MR examinations were conducted on a 1.5 T system in 68 patients prior to the initiation of neoadjuvant chemotherapy. DCE-MRI consisted of a fast spoiled gradient echo sequence acquired over 35 phases with a mean temporal resolution of 11.3 s. Both pharmacokinetic and empirical parameters were derived from the DCE-MRI data. Kaplan-Meier survival plots were generated for each parameter and group comparisons were made utilising logrank tests. The results from the 54 patients entered into the univariate survival analysis demonstrated that traditional prognostic parameters (tumour grade, hormonal status and size), empirical parameters (maximum enhancement index, enhancement index at 30 s, area under the curve and initial slope) and adjuvant therapies demonstrated significant differences in survival intervals. Further multivariate Cox regression survival analysis revealed that empirical enhancement parameters contributed the greatest prediction of both DFS and OS in the resulting models. In conclusion, this study has demonstrated that in patients who exhibit high levels of perfusion and vessel permeability pre-treatment, evidenced by elevated empirical DCE-MRI parameters, a significantly lower disease free survival and overall survival can be expected.

Full Text Available The aim of this study was to predict Ki-67 labeling index (LI preoperatively by three-dimensional (3D CT image parameters for pathologic assessment of GGO nodules. Diameter, total volume (TV, the maximum CT number (MAX, average CT number (AVG and standard deviation of CT number within the whole GGO nodule (STD were measured by 3D CT workstation. By detection of immunohistochemistry and Image Software Pro Plus 6.0, different Ki-67 LI were measured and statistically analyzed among preinvasive adenocarcinoma (PIA, minimally invasive adenocarcinoma (MIA and invasive adenocarcinoma (IAC. Receiver operating characteristic (ROC curve, Spearman correlation analysis and multiple linear regression analysis with cross-validation were performed to further research a quantitative correlation between Ki-67 labeling index and radiological parameters. Diameter, TV, MAX, AVG and STD increased along with PIA, MIA and IAC significantly and consecutively. In the multiple linear regression model by a stepwise way, we obtained an equation: prediction of Ki-67 LI=0.022*STD+0.001* TV+2.137 (R=0.595, R's square=0.354, p<0.001, which can predict Ki-67 LI as a proliferative marker preoperatively. Diameter, TV, MAX, AVG and STD could discriminate pathologic categories of GGO nodules significantly. Ki-67 LI of early lung adenocarcinoma presenting GGO can be predicted by radiologic parameters based on 3D CT for differential diagnosis.

It is important to predict progressive deficit (PD) in isolated pontine infarction, a relatively common problem of clinical stroke practice. Traditionally, lacunar infarctions are known with their progressive course. However, few studies have analyzed the branch atheromatous disease subtype as a subtype of lacunar infarction, separately. There are also conflicting results regarding the relationship with the topography of lesion and PD. In this study, we classified etiological subtypes and lesion topography in isolated pontine infarction and aimed to investigate the association of etiological subtypes, lesion topography and clinical outcome with PD. We analyzed demographics, laboratory parameters, and risk factors of 120 patients having isolated pontine infarction and admitted within 24 h retrospectively. PD was defined as an increase in the National Institutes of Health Stroke scale ≥2 units in 5 days after onset. Patients were classified as following: large artery disease (LAA), basilar artery branch disease (BABD) and small vessel disease (SVD). Upper, middle and lower pontine infarcts were identified longitudinally. Functional outcome at 3 months was determined according to modified Rankin scores. Of 120 patients, 41.7% of the patients were classified as BABD, 30.8% as SVD and 27.5% as LAA. 23 patients (19.2%) exhibited PD. PD was significantly more frequent in patient with BABD (p 0.006). PD was numerically higher in patients with lower pontine infarction. PD was associated with BABD and poor functional outcome. It is important to discriminate the BABD neuroradiologically from other stroke subtypes to predict PD which is associated with poor functional outcome in patients with isolated pontine infarctions.

Full Text Available Sperm is particularly susceptible to reactive oxygen species (ROS during critical phases of spermiogenesis. However, the level of seminal ROS is restricted by seminal antioxidants which have beneficial effects on sperm parameters and developmental potentials. Mitochondria and sperm plasma membrane are two major sites of ROS generation in sperm cells. Besides, leukocytes including polymer phonuclear (PMN leukocytes and macrophages produce broad category of molecules including oxygen free radicals, non-radical species and reactive nitrogen species. Physiological role of ROS increase the intracellular cAMP which then activate protein kinase in male reproductive system. This indicates that spermatozoa need small amounts of ROS to acquire the ability of nuclear maturation regulation and condensation to fertilize the oocyte. There is a long list of intrinsic and extrinsic factors which can induce oxidative stress to interact with lipids, proteins and DNA molecules. As a result, we have lipid peroxidation, DNA fragmentation, axonemal damage, denaturation of the enzymes, over generation of superoxide in the mitochondria, lower antioxidant activity and finally abnormal spermatogenesis. If oxidative stress is considered as one of the main cause of DNA damage in the germ cells, then there should be good reason for antioxidant therapy in these conditions

for corresponding standard parameters, but they were not significantly different from each other. CONCLUSION: SPECT-based functional parameters were better to predict the risk of RP compared to standard CT-based dose-volume parameters. Functional parameters may be useful to guide radiotherapy planning in order...

We present an analytical approach based on Cramer-Rao Bounds (CRBs) to investigate the uncertainties in estimated ocean color parameters resulting from the propagation of uncertainties in the bio-optical reflectance modeling through the inversion process. Based on given bio-optical and noise probabilistic models, CRBs can be computed efficiently for any set of ocean color parameters and any sensor configuration, directly providing the minimum estimation variance that can be possibly attained by any unbiased estimator of any targeted parameter. Here, CRBs are explicitly developed using (1) two water reflectance models corresponding to deep and shallow waters, resp., and (2) four probabilistic models describing the environmental noises observed within four Sentinel-2 MSI, HICO, Sentinel-3 OLCI and MODIS images, resp. For both deep and shallow waters, CRBs are shown to be consistent with the experimental estimation variances obtained using two published remote-sensing methods, while not requiring one to perform any inversion. CRBs are also used to investigate to what extent perfect a priori knowledge on one or several geophysical parameters can improve the estimation of remaining unknown parameters. For example, using pre-existing knowledge of bathymetry (e.g., derived from LiDAR) within the inversion is shown to greatly improve the retrieval of bottom cover for shallow waters. Finally, CRBs are shown to provide valuable information on the best estimation performances that may be achieved with the MSI, HICO, OLCI and MODIS configurations for a variety of oceanic, coastal and inland waters. CRBs are thus demonstrated to be an informative and efficient tool to characterize minimum uncertainties in inverted ocean color geophysical parameters.

Participation in day-to-day activities of people with schizophrenia is restricted, causing concern to them, their families, service providers and the communities at large. Participation is a significant component of health and recovery; however, factors predicting participation are still not well established. This study examines whether the parameters obtained during acute hospitalization can predict the intensity and diversity of participation in day-to-day activities six months after discharge. In-patients with chronic schizophrenia (N = 104) were enrolled into the study and assessed for cognitive functioning, functional capacity in instrumental activities of daily living (IADL), and symptoms. Six months after discharge, the intensity and diversity of participation in day-to-day activities were evaluated (N = 70). Multiple correlations were found between parameters obtained during hospitalization and participation diversity, but not participation intensity. The model that is better suited to the prediction of participation diversity contains cognitive ability of construction, negative symptoms and number of previous hospitalizations. The total explained variance is 37.8% (F 3,66 = 14.99, p process for the prediction of participation diversity in day-to-day activities six months after discharge. Participation diversity is best predicted through a set of factors reflecting personal and environmental indicators. Implications for rehabilitation Results of in-patient evaluations can predict the diversity of participation in day-to-day activities six months after discharge. Higher prediction of participation diversity is obtained using a holistic evaluation model that includes assessments for cognitive abilities, negative symptoms severity and number of hospitalizations.

Full Text Available In the article the data on the study of the etiological factors of various types of chronic gastritis in children are presented. Based on revealing of the auto antibodies to parietal gastric cells in 40,0% of children autoimmune gastritis (a type gastritis is diagnosed. Helicobacterr pylori infection is revealed in 44,8% of children. In 27,6% of children type c gastritis is diagnosed. Autoimmune gastritis in children has been linked to the active phase of chronic epsteinbbarr virus infection. the etiological factors of nonautoimmune gastritis are Helicobacter pylori infection (type b gastritis and multiple duodenogastric refluxes (type c gastritis.Key words: children, chronic gastritis, etiological factors, autoimmune gastritis, nonautoimmune gastritis, active phase of chronic Epstein-Barr virus infection, Helicobacter pylori infection.

Conclusions: The predictive value of distinct MRI-parameters differs for specific domains of cognitive function, with a greater impact of cortical volume, focal and diffuse white matter abnormalities on overall cognitive function, an additional role of basal ganglia iron deposition on cognitive efficiency, and thalamic and hippocampal volume on memory function. This suggests the usefulness of using multiparametric MRI to assess (microstructural correlates of different cognitive constructs.

Full Text Available Background: Infertility is a common gynaecological problem and male factor contributes significantly in the aetiology of infertility. Semen analysis has remained a useful investigation in the search for male factor infertility. Aim: This study assessed the pattern of semen parameters and predictive factors associated with abnormal parameters in male partners of infertile couples attending a Nigerian tertiary hospital. Methods: A descriptive study of infertile couples presenting at the clinic between January 2012and December 2015 was done at Ekiti State University Teaching Hospital, Ado-Ekiti. Seminal fluid from the male partners were analysed in the laboratory using the WHO 2010 criteria for human semen characteristics. Data was analysed using SPSS 17 and logistic regression analysis was used to determine the predictive factors associated with abnormal semen parameters. Results: A total of 443 men participated in the study and 38.2% had abnormal sperm parameters. Oligozoospermia (34.8% and asthenozoospermia (26.9% are leading single factor abnormality found, astheno-oligozoospermia occurred in 14.2% and oligo-astheno-teratozoospermia in 3.6% of cases. The prevalence of azoospermia was 3.4%. Smoking habit, past infection with mumps and previous groin surgery significantly predicted abnormal semen parameters with p values of 0.025, 0.040 and 0.017 respectively. Positive cultures were recorded in 36.2% of cases and staph aureus was the commonest organism. Conclusion: Male factor abnormalities remain significant contributors to infertility and men should be encouraged through advocacy to participate in investigation of infertility to reduce the level of stigmatization and ostracizing of women with infertility especially in sub-Saharan Africa.

Full Text Available Background: Infertility is a common gynaecological problem and male factor contributes significantly in the aetiology of infertility. Semen analysis has remained a useful investigation in the search for male factor infertility.Aim: This study assessed the pattern of semen parameters and predictive factors associated with abnormal parameters in male partners of infertile couples attending a Nigerian tertiary hospital.Methods: A descriptive study of infertile couples presenting at the clinic between January 2012and December 2015 was done at Ekiti State University Teaching Hospital, Ado-Ekiti. Â Seminal fluid from the male partners were analysed in the laboratory using the WHO 2010 criteria for human semen characteristics. Data was analysed using SPSS 17 and logistic regression analysis was used to determine the predictive factors associated with abnormal semen parameters.Results: A total of 443 men participated in the study and 38.2% had abnormal sperm parameters. Oligozoospermia (34.8% and asthenozoospermia (26.9% are leading single factor abnormality found, astheno-oligozoospermia occurred in 14.2% and oligo-astheno-teratozoospermia in 3.6% of cases. The prevalence of azoospermia was 3.4%. Smoking habit, past infection with mumps and previous groin surgery significantly predicted abnormal semen parameters with p values of 0.025, 0.040 and 0.017 respectively. Positive cultures were recorded in 36.2% of cases and staph aureus was the commonest organism.Conclusion: Male factor abnormalities remain significant contributors to infertility and men should be encouraged through advocacy to participate in investigation of infertility to reduce the level of stigmatization and ostracizing of women with infertility especially in sub-Saharan Africa.

Full Text Available Background: Vocal fold polyp is one of the most common causes for hoarseness. Many different etiological factors contribute to vocal fold polyp formation. The aim of the study was to find out whether the etiological factors for polyp formation have changed in the last 30 years.Methods: Eighty-one patients with unilateral vocal fold polyp were included in the study. A control group was composed of 50 volunteers without voice problems who matched the patients by age and gender. The data about etiological factors and the findings of phoniatric examination were obtained from the patients' medical documentation and from the questionnaires for the control group. The incidence of etiological factors was compared between the two groups. The program SPSS, Version 18 was used for statistical analysis.Results: The most frequent etiological factors were occupational voice load, GER, allergy and smoking. In 79% of patients 2 – 6 contemporary acting risk factors were found. Occupational voice load (p=0,018 and GER (p=0,004 were significantly more frequent in the patients than in the controls. The other factors did not significantly influence the polyp formation.Conclusions: There are several factors involved simultaneously in the formation of vocal fold polyps both nowadays and 30 years ago. Some of the most common factors remain the same (voice load, smoking, others are new (GER, allergy, which is probably due to the different lifestyle and working conditions than 30 years ago. Occupational voice load and GER were significantly more frequently present in the patients with polyp than in the control group. Regarding the given results it is important to instruct workers with professional vocal load about etiological factors for vocal fold polyp formation.

The cycling and static strengths of a wide range of high-strength steels have been experimentally tested. Correlation between the three parameters-microplastic deformation, strain hardening coefficient, and the slope of the curve to the axis of load cycles-has been established [ru

In this study, a carbonate field from Iran was studied. Estimation of rock properties such as porosity and permeability is much more challenging in carbonate rocks than sandstone rocks because of their strong heterogeneity. The frame flexibility factor (γ) is a rock physics parameter which is related not only to pore structure variation but also to solid/pore connectivity and rock texture in carbonate reservoirs. We used porosity, frame flexibility factor and bulk modulus of fluid as the proper parameters to study this gas carbonate reservoir. According to rock physics parameters, three facies were defined: favourable and unfavourable facies and then a transition facies located between these two end members. To capture both the inversion solution and associated uncertainty, a complete implementation of the Bayesian inversion of the facies from pre-stack seismic data was applied to well data and validated with data from another well. Finally, this method was applied on a 2D seismic section and, in addition to inversion of petrophysical parameters, the high probability distribution of favorable facies was also obtained. (paper)

Conclusion: The most widely used nomogram is of high value in therapy decision-making, although it remains an auxiliary means. Considering the performance of lymph node dissection, surgeons should be aware of the specifics of the applied nomogram. PSAD appears as a useful adjunctive parameter for preoperative prostate risk estimation and warrants further evaluation.

Full Text Available Numerous experimental fracture healing studies are performed on rats, in which different experimental, mechanical parameters are applied, thereby prohibiting direct comparison between each other. Numerical fracture healing simulation models are able to predict courses of fracture healing and offer support for pre-planning animal experiments and for post-hoc comparison between outcomes of different in vivo studies. The aims of this study are to adapt a pre-existing fracture healing simulation algorithm for sheep and humans to the rat, to corroborate it using the data of numerous different rat experiments, and to provide healing predictions for future rat experiments. First, material properties of different tissue types involved were adjusted by comparing experimentally measured callus stiffness to respective simulated values obtained in three finite element (FE models. This yielded values for Young's moduli of cortical bone, woven bone, cartilage, and connective tissue of 15,750 MPa, 1,000 MPa, 5 MPa, and 1 MPa, respectively. Next, thresholds in the underlying mechanoregulatory tissue differentiation rules were calibrated by modifying model parameters so that predicted fracture callus stiffness matched experimental data from a study that used rigid and flexible fixators. This resulted in strain thresholds at higher magnitudes than in models for sheep and humans. The resulting numerical model was then used to simulate numerous fracture healing scenarios from literature, showing a considerable mismatch in only 6 of 21 cases. Based on this corroborated model, a fit curve function was derived which predicts the increase of callus stiffness dependent on bodyweight, fixation stiffness, and fracture gap size. By mathematically predicting the time course of the healing process prior to the animal studies, the data presented in this work provides support for planning new fracture healing experiments in rats. Furthermore, it allows one to transfer and

in soils is strongly controlled by the soil structure, the capabilities of these visualization techniques could be used to predict the risk of pollutants leaching. This work was carried out using soils from a field site (Hygum) in Jutland, Denmark, a historical copper (Cu) polluted field cultivated for 80...

The anaerobic degradation pathway of hexachlorobenzene starts with a series of reductive dehalogenation steps. In the present paper it was evaluated whether the dehalogenation pathway observed in microbial ecosystems could be predicted by the redox potential and/or the reduction potential (the

The ability to predict water quality in lakes is important since lakes are sources of water for agriculture, drinking, and recreational uses. Lakes are also home to a dynamic ecosystem of lacustrine wetlands and deep waters. They are sensitive to pH changes and are dependent on d...

In conclusion, nutritional state and chronic comorbidities were major factors predicting the clinical outcome of severe AKI patients requiring CRRT. CKM was a simple and useful method in the assessment of nutritional state during CRRT treatment. Low creatinine production reflecting poor nutrition and protein reserve was associated with poor prognosis in severely ill ARF patients.

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

The paper presents results of testing a modified algorithm for predicting virus ID50 values in a host of interest by extrapolation from a model host taking into account immune neutralizing factors and thermal inactivation of the virus. The method was tested for A/Aichi/2/68 influenza virus in SPF Wistar rats, SPF CD-1 mice and conventional ICR mice. Each species was used as a host of interest while the other two served as model hosts. Primary lung and trachea cells and secretory factors of the rats' airway epithelium were used to measure parameters needed for the purpose of prediction. Predicted ID50 values were not significantly different (p = 0.05) from those experimentally measured in vivo. The study was supported by ISTC/DARPA Agreement 450p.

The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady state signal is generated by a pseudo-random excitation pattern. A comparison of the measured signal in each voxel with the physical model yields quantitative parameter maps. Currently, the comparison is done by matching a dictionary of simulated signals to the acquired signals. To accelerate the computation of quantitative maps we train a Convolutional Neural Network (CNN) on simulated dictionary data. As a proof of principle we show that the neural network implicitly encodes the dictionary and can replace the matching process.

Risk-taking behavior increases during adolescence, leading to potentially disastrous consequences. Social anxiety emerges in adolescence and may compound risk-taking propensity, particularly during stress and when reward potential is high. However, the manner in which social anxiety, stress, and reward parameters interact to impact adolescent risk-taking is unclear. To clarify this question, a community sample of 35 adolescents (15 to 18 yo), characterized as having high or low social anxiety...

ABSTRACT Background: An accurate assessment of the degree of dehydration in infants and children is important for proper decision-making and treatment. This emphasizes the need for laboratory tests to improve the accuracy of clinical assessment of dehydration. The aim of this study was to assess the relationship between clinical and laboratory parameters in the assessment of dehydration. Methods: We evaluated prospectively 200 children aged 1 month to 5 years who presented with diarrhea, vomi...

One of the key issues of recent research on the dispersion inside complex urban environments is the ability to predict dosage-based parameters from the puff release of an airborne material from a point source in the atmospheric boundary layer inside the built-up area. The present work addresses the question of whether the computational fluid dynamics (CFD)-Reynolds-averaged Navier-Stokes (RANS) methodology can be used to predict ensemble-average dosage-based parameters that are related with the puff dispersion. RANS simulations with the ADREA-HF code were, therefore, performed, where a single puff was released in each case. The present method is validated against the data sets from two wind-tunnel experiments. In each experiment, more than 200 puffs were released from which ensemble-averaged dosage-based parameters were calculated and compared to the model's predictions. The performance of the model was evaluated using scatter plots and three validation metrics: fractional bias, normalized mean square error, and factor of two. The model presented a better performance for the temporal parameters (i.e., ensemble-average times of puff arrival, peak, leaving, duration, ascent, and descent) than for the ensemble-average dosage and peak concentration. The majority of the obtained values of validation metrics were inside established acceptance limits. Based on the obtained model performance indices, the CFD-RANS methodology as implemented in the code ADREA-HF is able to predict the ensemble-average temporal quantities related to transient emissions of airborne material in urban areas within the range of the model performance acceptance criteria established in the literature. The CFD-RANS methodology as implemented in the code ADREA-HF is also able to predict the ensemble-average dosage, but the dosage results should be treated with some caution; as in one case, the observed ensemble-average dosage was under-estimated slightly more than the acceptance criteria. Ensemble

Full Text Available Koki Tsukamoto1, Tatsuya Yoshikawa1,2, Kiyonobu Yokota1, Yuichiro Hourai1, Kazuhiko Fukui11Computational Biology Research Center (CBRC, National Institute of Advanced Industrial Science and Technology (AIST, Koto-ku, Tokyo, Japan; 2Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Toyonaka, Osaka, JapanAbstract: A system was developed to evaluate and predict the interaction between protein pairs by using the widely used shape complementarity search method as the algorithm for docking simulations between the proteins. We used this system, which we call the affinity evaluation and prediction (AEP system, to evaluate the interaction between 20 protein pairs. The system first executes a “round robin” shape complementarity search of the target protein group, and evaluates the interaction between the complex structures obtained by the search. These complex structures are selected by using a statistical procedure that we developed called ‘grouping’. At a prevalence of 5.0%, our AEP system predicted protein–protein interactions with a 50.0% recall, 55.6% precision, 95.5% accuracy, and an F-measure of 0.526. By optimizing the grouping process, our AEP system successfully predicted 10 protein pairs (among 20 pairs that were biologically relevant combinations. Our ultimate goal is to construct an affinity database that will provide cell biologists and drug designers with crucial information obtained using our AEP system.Keywords: protein–protein interaction, affinity analysis, protein–protein docking, FFT, massive parallel computing

Full Text Available Radiation esophagitis (RE is a common adverse event associated with radiotherapy for non–small cell lung cancer (NSCLC. While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc and generalized equivalent uniform dose (gEUD], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc and 0.727 (gEUD. Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning.

Full Text Available BackgroundThe etiology of inguinal hernias remains uncertain even though the lifetime risk of developing an inguinal hernia is 27% for men and 3% for women. The aim was to summarize the evidence on hernia etiology, with focus on differences between lateral and medial hernias.ResultsLateral and medial hernias seem to have common as well as different etiologies. A patent processus vaginalis and increased cumulative mechanical exposure are risk factors for lateral hernias. Patients with medial hernias seem to have a more profoundly altered connective tissue architecture and homeostasis compared with patients with lateral hernias. However, connective tissue alteration may play a role in development of both subtypes. Inguinal hernias have a hereditary component with a complex inheritance pattern, and inguinal hernia susceptible genes have been identified that also are involved in connective tissue homeostasis.ConclusionThe etiology of lateral and medial hernias are at least partly different, but the final explanations are still lacking on certain areas. Further investigations of inguinal hernia genes may explain the altered connective tissue observed in patients with inguinal hernias. The precise mechanisms why processus vaginalis fails to obliterate in certain patients should also be clarified. Not all patients with a patent processus vaginalis develop a lateral hernia, but increased intraabdominal pressure appears to be a contributing factor.

This study aims to define symptoms and etiology and determine how to prevent chronic rhinosinusitis in children. Between February 2003 and February 2005, 50 pediatric patients (25 girls and 25 boys; mean age 8.22 years; range 4 to 14 years) with chronic rhinosinusitis were included in the study. The patients were questioned about anterior/posterior nasal dripping, night cough, headache, nausea, vomiting and nasal obstruction for symptomatology; about school condition, smoking behavior of parents and history of asthma for etiology. Hemogram, serum biochemistry, allergy test, nasal smear, chest and lateral neck radiography and sweat test were performed. Symptomatologic examination revealed that 48% had anterior nasal dripping, 62% with postnasal dripping, 70% with headache and 90% with nasal obstruction. Evaluation of etiological factors revealed that 68% were going to school, 48% of the parents had the history of smoking, 42% with allergy test-positivity and 60% with adenoid vegetation. Our study results indicated that environmental factors are important as etiological factors in rhinosinusitis. For prevention, we recommend restriction of close relationship at school, not to smoke at home and vaccination in each year with influenza and S. pneumonia vaccine.

This paper focuses on the common sources of etiologies of conflict in multicultural contexts. Multicultural communication is the creation and sharing of meaning among citizens of the same geopolitical system who belong to divergent tributary cultures. The sources of conflict in multicultural relations can be grouped into five broad categories.…

Five cases of chronic, inflammatory, multifocal bone lesions of unknown etiology are reported. Although bone biopsy confirmed osteomyelitis in each case in none of them were organisms found inspite of an extensive work up. Different clinical course of the disease reflects different aetiology in respective cases. These cases present changing aspects of osteomyelitis emerging since introduction of antibiotics. (orig.)

Reviews recent research on the etiologies of autism, including genetic research, anatomic and neuroimaging studies, topics in neurophysiology research (including serotonin, dopamine, and opiods), immunologic research, studies of autism phenotype, and electroencephalographic studies. It concludes that, as of yet, research has found no clear…

Lack of consideration of the sexually functional population has led to misconceptions about causes of sexual dysfunction functioning. Automatic functioning can mask effects of pathogenic influences on sexuality, making these effects appear random, confounding etiological issues and creating the belief that causes of sexual dysfunction and disorder…

Jun 26, 2015 ... Results: In the majority of cases, the disease evolved without the presence of associated systemic disorders (60% [45.49-. 72.69]) ... immune deficiencies, chronic alcoholism, or hepatic ... of these reports regarding the etiology of the development ..... periodontal lesions, where the Streptococcus strains are.

Thermal modelling is used to examine the subsurface temperature field and geothermal conditions at various scales (e.g. sedimentary basins, deep crust) and in the framework of different problem settings (e.g. scientific or industrial use). In such models, knowledge of rock thermal properties is prerequisites for the parameterisation of boundary conditions and layer properties. In contrast to hydrogeological ground-water models, where parameterization of the major rock property (i.e. hydraulic conductivity) is generally conducted considering lateral variations within geological layers, parameterization of thermal models (in particular regarding thermal conductivity but also radiogenic heat production and specific heat capacity) in most cases is conducted using constant parameters for each modelled layer. For such constant thermal parameter values, moreover, initial values are normally obtained from rare core measurements and/or literature values, which raise questions for their representativeness. Some few studies have considered lithological composition or well log information, but still keeping the layer values constant. In the present thermal-modelling scenario analysis, we demonstrate how the use of different parameter input type (from literature, well logs and lithology) and parameter input style (constant or laterally varying layer values) affects the temperature model prediction in sedimentary basins. For this purpose, rock thermal properties are deduced from standard petrophysical well logs and lithological descriptions for several wells in a project area. Statistical values of thermal properties (mean, standard deviation, moments, etc.) are calculated at each borehole location for each geological formation and, moreover, for the entire dataset. Our case study is located at the Danish-German border region (model dimension: 135 x115 km, depth: 20 km). Results clearly show that (i) the use of location-specific well-log derived rock thermal properties and (i

Purpose: To investigate the plasma dynamics of 5 proinflammatory/fibrogenic cytokines, including interleukin-1beta (IL-1{beta}), IL-6, IL-8, tumor necrosis factor alpha (TNF-{alpha}), and transforming growth factor beta1 (TGF-{beta}1) to ascertain their value in predicting radiation-induced lung toxicity (RILT), both individually and in combination with physical dosimetric parameters. Methods and Materials: Treatments of patients receiving definitive conventionally fractionated radiation therapy (RT) on clinical trial for inoperable stages I-III lung cancer were prospectively evaluated. Circulating cytokine levels were measured prior to and at weeks 2 and 4 during RT. The primary endpoint was symptomatic RILT, defined as grade 2 and higher radiation pneumonitis or symptomatic pulmonary fibrosis. Minimum follow-up was 18 months. Results: Of 58 eligible patients, 10 (17.2%) patients developed RILT. Lower pretreatment IL-8 levels were significantly correlated with development of RILT, while radiation-induced elevations of TGF-ss1 were weakly correlated with RILT. Significant correlations were not found for any of the remaining 3 cytokines or for any clinical or dosimetric parameters. Using receiver operator characteristic curves for predictive risk assessment modeling, we found both individual cytokines and dosimetric parameters were poor independent predictors of RILT. However, combining IL-8, TGF-ss1, and mean lung dose into a single model yielded an improved predictive ability (Pprediction. Future study with a larger number of cases and events is needed to validate such findings.

One of the main disadvantages of the recently reported method, for setting up the drying regime based on the theory of moisture migration during drying, lies in a fact that it is based on a large number of isothermal experiments. In addition each isothermal experiment requires the use of different drying air parameters. The main goal of this paper was to find a way how to reduce the number of isothermal experiments without affecting the quality of the previously proposed calculation method. The first task was to define the lower and upper inputs as well as the output of the “black box” which will be used in the Box-Wilkinson’s orthogonal multi-factorial experimental design. Three inputs (drying air temperature, humidity and velocity) were used within the experimental design. The output parameter of the model represents the time interval between any two chosen characteristic points presented on the Deff - t. The second task was to calculate the output parameter for each planed experiments. The final output of the model is the equation which can predict the time interval between any two chosen characteristic points as a function of the drying air parameters. This equation is valid for any value of the drying air parameters which are within the defined area designated with lower and upper limiting values.

We present a new method for calculating broadband time histories of ground motion based on a hybrid low-frequency/high-frequency approach with correlated source parameters. Using a finite-difference method we calculate low- frequency synthetics (structure. We also compute broadband synthetics in a 1D velocity model using a frequency-wavenumber method. The low frequencies from the 3D calculation are combined with the high frequencies from the 1D calculation by using matched filtering at a crossover frequency of 1 Hz. The source description, common to both the 1D and 3D synthetics, is based on correlated random distributions for the slip amplitude, rupture velocity, and rise time on the fault. This source description allows for the specification of source parameters independent of any a priori inversion results. In our broadband modeling we include correlation between slip amplitude, rupture velocity, and rise time, as suggested by dynamic fault modeling. The method of using correlated random source parameters is flexible and can be easily modified to adjust to our changing understanding of earthquake ruptures. A realistic attenuation model is common to both the 3D and 1D calculations that form the low- and high-frequency components of the broadband synthetics. The value of Q is a function of the local shear-wave velocity. To produce more accurate high-frequency amplitudes and durations, the 1D synthetics are corrected with a randomized, frequency-dependent radiation pattern. The 1D synthetics are further corrected for local site and nonlinear soil effects by using a 1D nonlinear propagation code and generic velocity structure appropriate for the site’s National Earthquake Hazards Reduction Program (NEHRP) site classification. The entire procedure is validated by comparison with the 1994 Northridge, California, strong ground motion data set. The bias and error found here for response spectral acceleration are similar to the best results that have been published by

Full Text Available We sought to imitate angiographic cerebral circulation time (CCT and create a similar index from baseline CT perfusion (CTP to better predict vasospasm in patients with subarachnoid hemorrhage (SAH.Forty-one SAH patients with available DSA and CTP were retrospectively included. The vasospasm group was comprised of patients with deterioration in conscious functioning and newly developed luminal narrowing; remaining cases were classified as the control group. The angiography CCT (XA-CCT was defined as the difference in TTP (time to peak between the selected arterial ROIs and the superior sagittal sinus (SSS. Four arterial ROIs were selected to generate four corresponding XA-CCTs: the right and left anterior cerebral arteries (XA-CCTRA2 and XA-CCTLA2 and right- and left-middle cerebral arteries (XA-CCTRM2 and XA-CCTLM2. The CCTs from CTP (CT-CCT were defined as the differences in TTP from the corresponding arterial ROIs and the SSS. Correlations of the different CCTs were calculated and diagnostic accuracy in predicting vasospasm was evaluated.Intra-class correlations ranged from 0.96 to 0.98. The correlations of XA-CCTRA2, XA-CCTRM2, XA-CCTLA2, and XA-CCTLM2 with the corresponding CT-CCTs were 0.64, 0.65, 0.53, and 0.68, respectively. All CCTs were significantly prolonged in the vasospasm group (5.8-6.4 s except for XA-CCTLA2. CT-CCTA2 of 5.62 was the optimal cut-off value for detecting vasospasm with a sensitivity of 84.2% and specificity 82.4.CT-CCTs can be used to interpret cerebral flow without deconvolution algorithms, and outperform both MTT and TTP in predicting vasospasm risk. This finding may help facilitate management of patients with SAH.

This study discusses about the biosorption of Cr(VI) ion from aqueous solution using ultrasonic assisted Spirulina platensis (UASP). The prepared UASP biosorbent was characterised by Fourier transform infrared spectroscopy, X-ray diffraction, Brunauer-Emmet-Teller, scanning electron spectroscopy and energy dispersive X-ray and thermogravimetric analyses. The optimum condition for the maximum removal of Cr(VI) ions for an initial concentration of 50 mg/l by UASP was measured as: adsorbent dose of 1 g/l, pH of 3.0, contact time of 30 min and temperature of 303 K. Adsorption isotherm, kinetics and thermodynamic parameters were calculated. Freundlich model provided the best results for the removal of Cr(VI) ions by UASP. The adsorption kinetics of Cr(VI) ions onto UASP showed that the pseudo-first-order model was well in line with the experimental data. In the thermodynamic study, the parameters like Gibb's free energy, enthalpy and entropy changes were evaluated. This result explains that the adsorption of Cr(VI) ions onto the UASP was exothermic and spontaneous in nature. Desorption of the biosorbent was done using different desorbing agents in which NaOH gave the best result. The prepared material showed higher affinity for the removal of Cr(VI) ions and this may be an alternative material to the existing commercial adsorbents.

Risk-taking behavior increases during adolescence, leading to potentially disastrous consequences. Social anxiety emerges in adolescence and may compound risk-taking propensity, particularly during stress and when reward potential is high. However, the manner in which social anxiety, stress, and reward parameters interact to impact adolescent risk-taking is unclear. To clarify this question, a community sample of 35 adolescents (15 to 18 yo), characterized as having high or low social anxiety, participated in a 2-day study, during each of which they were exposed to either a social stress or a control condition, while performing a risky decision-making task. The task manipulated, orthogonally, reward magnitude and probability across trials. Three findings emerged. First, reward magnitude had a greater impact on the rate of risky decisions in high social anxiety (HSA) than low social anxiety (LSA) adolescents. Second, reaction times (RTs) were similar during the social stress and the control conditions for the HSA group, whereas the LSA group’s RTs differed between conditions. Third, HSA adolescents showed the longest RTs on the most negative trials. These findings suggest that risk-taking in adolescents is modulated by context and reward parameters differentially as a function of social anxiety. PMID:25465884

Risk-taking behavior increases during adolescence, leading to potentially disastrous consequences. Social anxiety emerges in adolescence and may compound risk-taking propensity, particularly during stress and when reward potential is high. However, the manner in which social anxiety, stress, and reward parameters interact to impact adolescent risk-taking is unclear. To clarify this question, a community sample of 35 adolescents (15-18yo), characterized as having high or low social anxiety, participated in a study over two separate days, during each of which they were exposed to either a social stress or a control condition, while performing a risky decision-making task. The task manipulated, orthogonally, reward magnitude and probability across trials. Three findings emerged. First, reward magnitude had a greater impact on the rate of risky decisions in high social anxiety (HSA) than low social anxiety (LSA) adolescents. Second, reaction times (RTs) were similar during the social stress and the control conditions for the HSA group, whereas the LSA group's RTs differed between conditions. Third, HSA adolescents showed the longest RTs on the most negative trials. These findings suggest that risk-taking in adolescents is modulated by context and reward parameters differentially as a function of social anxiety. Published by Elsevier Ltd.

An increasing number of healthcare systems allow people to monitor behavior and provide feedback on health and wellness. Most applications, however, only offer feedback on behavior in form of visualization and data summaries. This paper presents a different approach—called impact factor analysis—...... in monitoring and control of mental illness, and we argue that the impact factor analysis can be useful in the design of other health and wellness systems....... ten bipolar patients, in which we were able to estimate mood values with an average mean absolute error of 0.5. This was used to rank the behavior parameters whose variations indicate changes in the mental state. The rankings acquired from our algorithms correspond to the patients’ rankings......, identifying physical activity and sleep as the highest impact parameters. These results revealed the feasibility of identifying behavioral impact factors. This data analysis motivated us to design an impact factor inference engine as part of the MONARCA system. To our knowledge, this is a novel approach...

A study has been conducted to evaluate how process parameters will exhibit the change in the event of the troubles related to reactor internal by using transient thermal-hydraulic analysis codes (RETRAN3D-MOD002, etc.). In the present study, the following six events are analytically investigated: 1) a leak from the upper plenum; 2) a leak from the middle part of a shroud; 3) a leak from the lower plenum; 4) a leak from the riser pipe for the jet-pump; 5) the blockage of the jet-pump nozzle; and 6) a leak from the jet-pump diffuser. The results by analyses indicated that the leak from the upper plenum resulted in increasing in the inlet temperature of primary loop recirculation (PLR) and in the differential pressure at the core support plate, and decreasing in the neutron flux (reactor power). Similar analyses were made for the five other events to identify the pattern of relevant process parameter variation in each event. (author)

This article focuses on the analysis of the behavior patterns of the variables involved in the anaerobic digestion process. The objective is to predict the impact factor and the behavior pattern of the variables, i.e., temperature, pH, volatile solids (VS), total solids, volumetric load, and hydraulic residence time, considering that these are the control variables for the conservation of the different groups of anaerobic microorganisms. To conduct the research, samples of physicochemical sludge were taken from a water treatment plant in a poultry processing factory, and, then, the substrate was characterized, and a thermal pretreatment was used to accelerate the hydrolysis process. The anaerobic digestion process was analyzed in order to obtain experimental data of the control variables and observe their impact on the production of biogas. The results showed that the thermal pre-hydrolysis applied at 90°C for 90 min accelerated the hydrolysis phase, allowing a significant 52% increase in the volume of methane produced. An artificial neural network was developed, and it was trained with the database obtained by monitoring the anaerobic digestion process. The results obtained from the artificial neural network showed that there is an adjustment between the real values and the prediction of validation based on 60 samples with a 96.4% coefficient of determination, and it was observed that the variables with the major impact on the process were the loading rate and VS, with impact factors of 36% and 23%, respectively.

Breast cancer can be classified into four molecular subtypes of Luminal A, Luminal B, HER2 and Basal-like, which have significant differences in treatment and survival outcomes. We in this study aim to predict immunohistochemistry (IHC) determined molecular subtypes of breast cancer using image features derived from tumor and peritumoral stroma region based on diffusion weighted imaging (DWI). A dataset of 126 breast cancer patients were collected who underwent preoperative breast MRI with a 3T scanner. The apparent diffusion coefficients (ADCs) were recorded from DWI, and breast image was segmented into regions comprising the tumor and the surrounding stromal. Statistical characteristics in various breast tumor and peritumoral regions were computed, including mean, minimum, maximum, variance, interquartile range, range, skewness, and kurtosis of ADC values. Additionally, the difference of features between each two regions were also calculated. The univariate logistic based classifier was performed for evaluating the performance of the individual features for discriminating subtypes. For multi-class classification, multivariate logistic regression model was trained and validated. The results showed that the tumor boundary and proximal peritumoral stroma region derived features have a higher performance in classification compared to that of the other regions. Furthermore, the prediction model using statistical features, difference features and all the features combined from these regions generated AUC values of 0.774, 0.796 and 0.811, respectively. The results in this study indicate that ADC feature in tumor and peritumoral stromal region would be valuable for estimating the molecular subtype in breast cancer.

Theoretical arguments demonstrate that practical considerations, including the needs to limit physiological resources and to learn without interference with prior learning, severely constrain the anatomical architecture of the brain. These arguments identify the hippocampal system as the change manager for the cortex, with the role of selecting the most appropriate locations for cortical receptive field changes at each point in time and driving those changes. This role results in the hippocampal system recording the identities of groups of cortical receptive fields that changed at the same time. These types of records can also be used to reactivate the receptive fields active during individual unique past events, providing mechanisms for episodic memory retrieval. Our theoretical arguments identify the perirhinal cortex as one important focal point both for driving changes and for recording and retrieving episodic memories. The retrieval of episodic memories must not drive unnecessary receptive field changes, and this consideration places strong constraints on neuron properties and connectivity within and between the perirhinal cortex and regular cortex. Hence the model predicts a number of such properties and connectivity. Experimental test of these falsifiable predictions would clarify how change is managed in the cortex and how episodic memories are retrieved. PMID:26819594

A generalized scheme is introduced for predicting impact sensitivity of any explosives by using artificial neural networks. Experimental values for the impact sensitivity for 291 compounds containing C, H, N and O have been used for training and testing sets. The input descriptors include aromatic character, heteroaromatic character, the number of N-NO{sub 2} bonds and the number of {alpha}-hydrogen atoms as well as the number of carbon, hydrogen, nitrogen, and oxygen divided by molecular weight. The reliability of the proposed model was assessed by comparing the results against measured values as well as five models of complicated quantum mechanical computed values of 14 CHNO explosives from a variety of chemical structures. The model gives root mean squares errors of 41 cm and 56 cm for training and test sets, respectively, of the H{sub 50} quantity. (Abstract Copyright [2006], Wiley Periodicals, Inc.)

The effects of constant heat exchanger area on the coefficient of performance (COP) for liquid-liquid heat pumps were analyzed for systems which use nonazeotropic mixtures as the working fluid. For this analysis, two different computer models were compared. In the first, the log mean temperature differences (LMTDs) through the heat exchangers were specified, and were held constant for all refrigerant compositions. The second method was constructed so that the heat exchanger UA product was held constant, thus approximating constant heat exchanger area over a range of refrigerant compositions. Results from these models show only a one percent difference in COP prediction between holding LMTD constant and holding UA constant over the range of mixture composition. This paper reports the models compared using mixtures of R-22/R-11 and R-22/R-114. It is also shown that changes in glide and lift temperatures have little influence on the differences between the two models

.05), while patients in the group II PET subset showed no statistical difference in OEF before surgery (0.58±0.14) and after surgery (0.54± 0.05). We conclude that the outcome of STA-MCA bypass surgery can be predicted by the improvement in resting CBF but not by the improvement in vasoreactivity. (orig.)

of preterm birth so it is logical to consider the use of antibiotics for the prevention of preterm birth. AREAS COVERED: Infection and antibiotics in the etiology, prediction and prevention of preterm birth. EXPERT OPINION: Antibiotics for the prevention of preterm birth have addressed different risk groups......, diagnostic methods, degrees of abnormal flora, antibiotic dose regimens, routes of administration, host susceptibilities, host response, gestational age at time of treatment, outcome parameters and definitions of success and outcomes. To address this confusion, a number of systematic reviews....../meta-analyses have been conducted but none has simultaneously addressed the optimal choice of agent, patient and timing of intervention. We conclude that inappropriate antibiotics used in inappropriate women at inappropriately late gestations do not reduce preterm birth. Conversely, a focused systematic review...

In this work the obtained results for a statistical analysis are shown, with the purpose of studying the performance of the fuel lattice, taking into account the frequency of the pins that were used. For this objective, different statistical distributions were used; one approximately to normal, another type X 2 but in an inverse form and a random distribution. Also, the prediction of some parameters of the nuclear reactor in a fuel reload was made through a neuronal network, which was trained. The statistical analysis was made using the parameters of the fuel lattice, which was generated through three heuristic techniques: Ant Colony Optimization System, Neuronal Networks and a hybrid among Scatter Search and Path Re linking. The behavior of the local power peak factor was revised in the fuel lattice with the use of different frequencies of enrichment uranium pines, using the three techniques mentioned before, in the same way the infinite multiplication factor of neutrons was analyzed (k..), to determine within what range this factor in the reactor is. Taking into account all the information, which was obtained through the statistical analysis, a neuronal network was trained; that will help to predict the behavior of some parameters of the nuclear reactor, considering a fixed fuel reload with their respective control rods pattern. In the same way, the quality of the training was evaluated using different fuel lattices. The neuronal network learned to predict the next parameters: Shutdown Margin (SDM), the pin burn peaks for two different fuel batches, Thermal Limits and the Effective Neutron Multiplication Factor (k eff ). The results show that the fuel lattices in which the frequency, which the inverted form of the X 2 distribution, was used revealed the best values of local power peak factor. Additionally it is shown that the performance of a fuel lattice could be enhanced controlling the frequency of the uranium enrichment rods and the variety of the gadolinium

The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2-35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.

Purpose: In an effort to develop noninvasive in vivo methods for mapping tumor oxygenation, magnetic resonance (MR)-derived parameters are being considered, including global R{sub 1}, water R{sub 1}, lipids R{sub 1}, and R{sub 2}*. R{sub 1} is sensitive to dissolved molecular oxygen, whereas R{sub 2}* is sensitive to blood oxygenation, detecting changes in dHb. This work compares global R{sub 1}, water R{sub 1}, lipids R{sub 1}, and R{sub 2}* with pO{sub 2} assessed by electron paramagnetic resonance (EPR) oximetry, as potential markers of the outcome of radiation therapy (RT). Methods and Materials: R{sub 1}, R{sub 2}*, and EPR were performed on rhabdomyosarcoma and 9L-glioma tumor models, under air and carbogen breathing conditions (95% O{sub 2}, 5% CO{sub 2}). Because the models demonstrated different radiosensitivity properties toward carbogen, a growth delay (GD) assay was performed on the rhabdomyosarcoma model and a tumor control dose 50% (TCD50) was performed on the 9L-glioma model. Results: Magnetic resonance imaging oxygen-sensitive parameters detected the positive changes in oxygenation induced by carbogen within tumors. No consistent correlation was seen throughout the study between MR parameters and pO{sub 2}. Global and lipids R{sub 1} were found to be correlated to pO{sub 2} in the rhabdomyosarcoma model, whereas R{sub 2}* was found to be inversely correlated to pO{sub 2} in the 9L-glioma model (P=.05 and .03). Carbogen increased the TCD50 of 9L-glioma but did not increase the GD of rhabdomyosarcoma. Only R{sub 2}* was predictive (Ppredictive marker under the same conditions. Conclusion: This work illustrates the sensitivity of oxygen-sensitive R{sub 1} and R{sub 2}* parameters to changes in tumor oxygenation. However, R{sub 1

Purpose: In an effort to develop noninvasive in vivo methods for mapping tumor oxygenation, magnetic resonance (MR)-derived parameters are being considered, including global R_1, water R_1, lipids R_1, and R_2*. R_1 is sensitive to dissolved molecular oxygen, whereas R_2* is sensitive to blood oxygenation, detecting changes in dHb. This work compares global R_1, water R_1, lipids R_1, and R_2* with pO_2 assessed by electron paramagnetic resonance (EPR) oximetry, as potential markers of the outcome of radiation therapy (RT). Methods and Materials: R_1, R_2*, and EPR were performed on rhabdomyosarcoma and 9L-glioma tumor models, under air and carbogen breathing conditions (95% O_2, 5% CO_2). Because the models demonstrated different radiosensitivity properties toward carbogen, a growth delay (GD) assay was performed on the rhabdomyosarcoma model and a tumor control dose 50% (TCD50) was performed on the 9L-glioma model. Results: Magnetic resonance imaging oxygen-sensitive parameters detected the positive changes in oxygenation induced by carbogen within tumors. No consistent correlation was seen throughout the study between MR parameters and pO_2. Global and lipids R_1 were found to be correlated to pO_2 in the rhabdomyosarcoma model, whereas R_2* was found to be inversely correlated to pO_2 in the 9L-glioma model (P=.05 and .03). Carbogen increased the TCD50 of 9L-glioma but did not increase the GD of rhabdomyosarcoma. Only R_2* was predictive (Ppredictive marker under the same conditions. Conclusion: This work illustrates the sensitivity of oxygen-sensitive R_1 and R_2* parameters to changes in tumor oxygenation. However, R_1 parameters showed limitations in terms of predicting the outcome of RT in the tumor models studied, whereas R_2* was found to be

The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V. parahaemolyticus in Alabama oysters to assess the utility of the former data for predicting V. parahaemolyticus densities in oysters. Remotely sensed sea surface temperature correlated well with previous in situ measurements (R(2) = 0.86) of bottom water temperature, supporting the notion that remotely sensed sea surface temperature data are a sufficiently accurate substitute for direct measurement. Turbidity and chlorophyll levels were not determined in the previous study, but in comparison with the V. parahaemolyticus data, remotely sensed values for these parameters may explain some of the variation in V. parahaemolyticus levels. More accurate determination of these effects and the temporal and spatial variability of these parameters may further improve the accuracy of prediction models. To illustrate the utility of remotely sensed data as a basis for risk management, predictions based on the U.S. Food and Drug Administration V. parahaemolyticus risk assessment model were integrated with remotely sensed sea surface temperature data to display graphically variations in V. parahaemolyticus density in oysters associated with spatial variations in water temperature. We believe images such as these could be posted in near real time, and that the availability of such information in a user-friendly format could be the basis for timely and informed risk management decisions.

Full Text Available Abstract Background Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Results Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Conclusion Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

Studies that evaluate gait rehabilitation programs for individuals with stroke often consider time since stroke of more than six months. In addition, most of these studies do not use lesion etiology or affected cerebral hemisphere as study factors. However, it is unknown whether these factors are associated with post-stroke motor performance after the spontaneous recovery period. To investigate whether time since stroke onset, etiology, and lesion side is associated with spatiotemporal and angular gait parameters of individuals with chronic stroke. Fifty individuals with chronic hemiparesis (20 women) were evaluated. The sample was stratified according to time since stroke (between 6 and 12 months, between 13 and 36 months, and over 36 months), affected cerebral hemisphere (left or right) and lesion etiology (ischemic and hemorrhagic). The participants were evaluated during overground walking at self-selected gait speed, and spatiotemporal and angular gait parameters were calculated. Results Differences between gait speed, stride length, hip flexion, and knee flexion were observed in subgroups stratified based on lesion etiology. Survivors of a hemorrhagic stroke exhibited more severe gait impairment. Subgroups stratified based on time since stroke only showed intergroup differences for stride length, and subgroups stratified based on affected cerebral hemisphere displayed between-group differences for swing time symmetry ratio. In order to recruit a more homogeneous sample, more accurate results were obtained and an appropriate rehabilitation program was offered, researchers and clinicians should consider that gait pattern might be associated with time since stroke, affected cerebral hemisphere and lesion etiology.

The purpose of this article was to briefly summarize the most important characteristics of non-carious cervical lesions, as well as the etiological factors that lead to their formation. Cervical area represents one of the most sensitive parts of the tooth due to the specific position, as well as the structure and thickness of hard tissue. It is less resistant to various chemical and mechanical stimuli, and as a result the lesions in this area are frequently encountered in everyday practice.

Autism and the other disorders in the autism spectrum are behaviorally defined syndromes that can be a prolonged disorder. The specific underlying neurophysiologic mechanisms simply not known, but probably several causes lead to disorders in the autism spectrum. This article is summary of recent research about etiology of autism but the search must continue. 1) Neurobiological origin, the neurobiological investigations show the role of dopamine and serotonin in pathogenesis of autism. 2) Gene...

Drash and Tudor describe six sets of reinforcement contingencies which may be present in the environments of some children eventually diagnosed with autism and suggest that these contingencies account for the etiology of “autistic” behaviors. Nevertheless, merely observing such contingencies in the environments of these children is insufficient to establish a positive correlation between the contingencies and “autistic” behaviors, let alone a causal relationship. To demonstrate a positive cor...

To determine the causes of short stature in children with special emphasis on growth hormone deficiency. Two hundred and fourteen children (140 boys and 74 girls), ranging from 02 to 15 years presenting with short stature were studied. Height and weight were plotted on appropriate growth charts and centiles determined. Relevant hematological and biochemical investigations including thyroid profile were done. Bone age was determined in all cases. Growth hormone axis was investigated after excluding other causes. Karyotyping was done in selected cases. Data was analyzed by SPSS 10.0 by descriptive statistics. Mean values were compared using t-test. In this study, the five most common etiological factors in order of frequency were Constitutional Growth Delay (CGD), Familial Short Stature (FSS), malnutrition, coeliac disease and Growth Hormone Deficiency (GHD). In 37.4% of patients, the study revealed normal variants of growth - CGD, FSS or combination of both, 46.7% cases had nonendocrinological and 15.9% had endocrinological etiology. CGD (22.1%) in males and FSS (27%) in females were the most common etiology. GHD was found in 6.1% children and it comprised 38.2% of all endocrinological causes. Children with height falling below 0.4th centile were more likely to have a pathological short stature (79.2%) compared to 39.3% whose height was below 3rd centile but above 0.4th centile (p<0.05). CGD and FSS are most common causes of short stature in boys and girls respectively, whereas, GHD is a relatively uncommon etiology. (author)

Full Text Available Leslie A Sadownik University of British Columbia, Department of Obstetrics and Gynecology, Vancouver, BC, Canada Abstract: Chronic vulvar pain or discomfort for which no obvious etiology can be found, ie, vulvodynia, can affect up to 16% of women. It may affect girls and women across all age groups and ethnicities. Vulvodynia is a significant burden to society, the health care system, the affected woman, and her intimate partner. The etiology is multifactorial and may involve local injury or inflammation, and peripheral and or central sensitization of the nervous system. An approach to the diagnosis and management of a woman presenting with chronic vulvar pain should address the biological, psychological, and social/interpersonal factors that contribute to her illness. The gynecologist has a key role in excluding other causes for vulvar pain, screening for psychosexual and pelvic floor dysfunction, and collaborating with other health care providers to manage a woman's pain. An important component of treatment is patient education regarding the pathogenesis of the pain and the negative impact of experiencing pain on a woman's overall quality of life. An individualized, holistic, and often multidisciplinary approach is needed to effectively manage the woman's pain and pain-related distress. Keywords: vulvodynia, diagnosis, treatment, etiology, sexual pain disorder, dyspareunia, vestibulodynia, assessment, treatment, multidisciplinary

Full Text Available Cerebrovasculer disease are the most frequent disease of the brain. Cerebellar infarct remains % 1.5-4.2 of these diseases. Etiological factors, lesion localization, symptoms and findings and relationship with prognosis of our patients with cerebellar infarct were investigated in our study. For this purpose, 32 patients were evaluated who were admitted to the Dicle University Medical School Department of Neurology in 1995-2001 hospitalized with the diagnosis of clinically and radiological confirmed cerebellar infarction.All of patients in the study group, 21 (%65.6 were male and 11 (%34.3 female. Age of overall patients ranged between 40 and 75 years with a mean of 57.8±10.2 years. Atherothrombotic infarct was the most frequent reason at the etiologic clinical classification. The most frequently found localization was the posterior inferior cerebellar artery infarct (%50. The leading two risk factors were hypertension (%78.1 and cigarette smoking (%50. The most common sign and symptoms were vertigo (%93.7, vomiting (%75, headache (%68.7 and cerebellar dysfunction findings (%50. The mean duration of hospitalization was 16.3±7.6 days. Overall mortality rate was found to be % 6.2. Finally, the most remarkable risk factors at cerebellar infarct patients are hypertension and atherosclerosis at etiology. We are considering that, controlling of these factors will reduce the appearance frequency of cerebellar infarcts.

Full Text Available Background: Bell’s palsy is the most frequent cause of unilateral facial paralysis. Inflammation is thought to play an important role in the pathogenesis of Bell’s palsy. Aims: Neutrophil to lymphocyte ratio (NLR and platelet to lymphocyte ratio (PLR are simple and inexpensive tests which are indicative of inflammation and can be calculated by all physicians. The aim of this study was to reveal correlations of Bell’s palsy and degree of paralysis with NLR and PLR. Study Design: Case-control study. Methods: The retrospective study was performed January 2010 and December 2013. Ninety-nine patients diagnosed as Bell’s palsy were included in the Bell’s palsy group and ninety-nine healthy individuals with the same demographic characteristics as the Bell’s palsy group were included in the control group. As a result of analyses, NLR and PLR were calculated. Results: The mean NLR was 4.37 in the Bell’s palsy group and 1.89 in the control group with a statistically significant difference (p<0.001. The mean PLR was 137.5 in the Bell’s palsy group and 113.75 in the control group with a statistically significant difference (p=0.008. No statistically significant relation was detected between the degree of facial paralysis and NLR and PLR. Conclusion: The NLR and the PLR were significantly higher in patients with Bell’s palsy. This is the first study to reveal a relation between Bell’s palsy and PLR. NLR and PLR can be used as auxiliary parameters in the diagnosis of Bell’s palsy.

ABSTRACT Background: An accurate assessment of the degree of dehydration in infants and children is important for proper decision-making and treatment. This emphasizes the need for laboratory tests to improve the accuracy of clinical assessment of dehydration. The aim of this study was to assess the relationship between clinical and laboratory parameters in the assessment of dehydration. Methods: We evaluated prospectively 200 children aged 1 month to 5 years who presented with diarrhea, vomiting or both. Dehydration assessment was done following a known clinical scheme. Results: We enrolled in the study 200 children (57.5% were male). The mean age was 15.62±9.03 months, with more than half those studied being under 24 months old. Overall, 46.5% (93) had mild dehydration, 34% (68) had moderate dehydration, 5.5% (11) had severe dehydration whereas, 14% (28) had no dehydration. Patients historical clinical variables in all dehydration groups did not differ significantly regarding age, sex, fever, frequency of vomiting, duration of diarrhea and vomiting, while there was a trend toward severe dehydration in children with more frequent diarrhea (p=0.004). Serum urea and creatinine cannot discriminate between mild and moderate dehydration but they showed a good specificity for severe dehydration of 99% and 100% respectively. Serum bicarbonates and base excess decreased significantly with a degree of dehydration and can discriminate between all dehydration groups (P<0.001). Conclusion: Blood gases were useful to diagnose the degree of dehydration status among children presenting with acute gastroenteritis. Serum urea and creatinine were the most specific tests for severe dehydration diagnosis. Historical clinical patterns apart from frequency of diarrhea did not correlate with dehydration status. Further studies are needed to validate our results. PMID:25568559

An accurate assessment of the degree of dehydration in infants and children is important for proper decision-making and treatment. This emphasizes the need for laboratory tests to improve the accuracy of clinical assessment of dehydration. The aim of this study was to assess the relationship between clinical and laboratory parameters in the assessment of dehydration. We evaluated prospectively 200 children aged 1 month to 5 years who presented with diarrhea, vomiting or both. Dehydration assessment was done following a known clinical scheme. We enrolled in the study 200 children (57.5% were male). The mean age was 15.62±9.03 months, with more than half those studied being under 24 months old. Overall, 46.5% (93) had mild dehydration, 34% (68) had moderate dehydration, 5.5% (11) had severe dehydration whereas, 14% (28) had no dehydration. Patients historical clinical variables in all dehydration groups did not differ significantly regarding age, sex, fever, frequency of vomiting, duration of diarrhea and vomiting, while there was a trend toward severe dehydration in children with more frequent diarrhea (p=0.004). Serum urea and creatinine cannot discriminate between mild and moderate dehydration but they showed a good specificity for severe dehydration of 99% and 100% respectively. Serum bicarbonates and base excess decreased significantly with a degree of dehydration and can discriminate between all dehydration groups (Pdehydration status among children presenting with acute gastroenteritis. Serum urea and creatinine were the most specific tests for severe dehydration diagnosis. Historical clinical patterns apart from frequency of diarrhea did not correlate with dehydration status. Further studies are needed to validate our results.

The aim of this study was to evaluate the histopathologic parameters that predict lymph node metastasis in patients with oral squamous cell carcinoma (OSCC) and to design a new assessment score on the basis of these parameters that could ultimately allow for changes in treatment decisions or aid clinicians in deciding whether there is a need for close follow-up or to perform early lymph node dissection. Histopathologic parameters of 336 cases of OSCC with stage cT1/T2 N0M0 disease were analyzed. The location of the tumor and the type of surgery used for the management of the tumor were recorded for all patients. The parameters, including T stage, grading of tumor, tumor budding, tumor thickness, depth of invasion, shape of tumor nest, lymphoid response at tumor-host interface and pattern of invasion, eosinophilic reaction, foreign-body giant cell reaction, lymphovascular invasion, and perineural invasion, were examined. Ninety-two patients had metastasis in lymph nodes. On univariate and multivariate analysis, independent variables for predicting lymph node metastasis in descending order were depth of invasion (P=0.003), pattern of invasion (P=0.007), perineural invasion (P=0.014), grade (P=0.028), lymphovascular invasion (P=0.038), lymphoid response (P=0.037), and tumor budding (P=0.039). We designed a scoring system on the basis of these statistical results and tested it. Cases with scores ranging from 7 to 11, 12 to 16, and ≥17 points showed LN metastasis in 6.4%, 22.8%, and 77.1% of cases, respectively. The difference between these 3 groups in relation to nodal metastasis was very significant (P<0.0001). A patient at low risk for lymph node metastasis (score, 7 to 11) had a 5-year survival of 93%, moderate-risk patients (score, 12 to 16) had a 5-year survival of 67%, and high-risk patients (score, 17 to 21) had a 5-year survival of 39%. The risk of lymph node metastasis in OSCC is influenced by many histologic parameters that are not routinely analyzed in

With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch. In a retrospective study, patients from 2 centers with proven stroke with onset value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly. In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and D